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Abstract In this paper, an elitist nondominated sorting hybrid algorithm, namely ENSHA, is proposed to solve the multi-objective flexible job-shop scheduling problem (MOFJSSP) with sequence-dependent setup times/costs (MOFJSSP_SDST/C). The objectives to be minimized are the maximal completion time (i.e., makespan) and the total setup costs (TSC). The makespan is an efficiency-focused objective whereas the TSC is an economic focused one. Existing works mainly consider the efficiency-focused multiple criteria. The main highlights of this paper are threefold, i.e., the operation-based sequence model, the problem-dependent job assignment rules and the novel evolutionary framework of ENSHA. For the operation-based sequence model, this is the first time that the sequence model of MOFJSSPs has been proposed and the TSC has been treated as an independent objective in MOFJSSPs. For the job assignment rules, the solution representation is first proposed, and then three job assignment rules are specifically designed to decode solutions or sequences into feasible scheduling schemes. For the novel evolutionary framework, it works with two populations, i.e., the main population (MP) and the auxiliary population (AP). First, ENSHA adopts the elitist nondominated sorting method for evolving MP to maintain high-quality solutions regarding both the convergence and diversity. Next, a machine learning strategy based on the estimation of distribution algorithm (EDA) is proposed to learn the valuable information from nondominated solutions in MP for building a probabilistic model. This model is then used to generate the offspring of AP. Furthermore, a simple yet effective cooperation-based refinement mechanism is raised to combine MP and AP, so as to generate MP of the next generation. Finally, experimental results on 39 benchmark instances and a real-life case study demonstrate the effectiveness and application values of the proposed ENSHA. [ABSTRACT FROM AUTHOR]

Abstract Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. Highlights • Grey wolf optimization algorithm is the object of study. • Defect of GWO is pointed out and the reason is determined. • Other optimization algorithms may have a similar defect. • The test method is proposed, and it works well. • Looking forward to the further development of optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among others) which allow an easy and fast development of new clustering algorithms. Furthermore, it includes 23 algorithms which represent the state-of-the-art in Evolutionary Algorithms for partial clustering. The paper describes through examples the main features and the design principles of the software, as well as how to use LEAC to carry out a comparison between different proposals and how to extend it by including new algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and practical vendor selection problem of a firm that cooperates with multiple geographically dispersed stores. In this problem, the firm entrusts some of its business process to external vendors, and each store can split the ordered quantity between one or more potential vendors, represented as a multi-sourcing strategies. Moreover, the Cobb–Douglas demand function is utilised to establish a relationship between the market demand and the selling price; representing price-sensitive demand. This paper seeks to choose the best vendors, to allocate the stores to them, and to find the optimal values for inventory-related decisions. The approach is based on the integration of the vendor selection problem and the inventory-related decisions in order to generate additional opportunities for system-wide operational efficiency and cost-effectiveness. The aim is to minimise total cost of the firm consisting of costs associated with the vendor selection and inventory-related decisions. A novel meta-heuristic called the binary-continuous invasive weed optimization (BCIWO) algorithm that is capable of solving both binary and continuous optimization problems is developed to solve the complicated NP-hard problem. As there is no benchmark available in the literature, an efficient genetic algorithm enhanced by a multi-parent crossover operator is designed to solve the problem in order to validate the results obtained using BCIWO. The algorithms are tuned using the response surface methodology, based on which their performances are analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for the end-to-end recognition of slab identification numbers (SINs). In the steel industry, automatic recognition of an individual product information is important for production management. The recognition of SINs in actual factory scenes is a challenging problem due to complicated background and low-quality of characters. Conventional rule-based algorithms were developed to extract information of SINs, but these methods require engineering knowledge and tedious work for parameter tuning. The proposed algorithm employs a data-driven method to overcome these limitations and to handle the challenges for the recognition of SINs. This paper proposes accumulated response map and model-based score function to effectively use the outputs of a deep convolutional neural network. Experiments were thoroughly conducted for industrial data collected from an actual steelworks to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that simultaneous recognition of entire characters in a SIN by optimizing the model-based score function is more effective for the robust performance compared to separated recognition of individual characters. [ABSTRACT FROM AUTHOR]

The main aim of feature subset selection is to find the minimum number of required features to perform classification without affecting the accuracy. It is one of the useful real-world applications for different types of classification datasets. Different feature subsets may achieve similar classification accuracy, which can help the user to select the optimal features. There are two main objectives involved in selecting a feature subset: minimizing the number of features and maximizing the accuracy. However, most of the existing studies do not consider multiple feature subsets of the same size. In this paper, we have proposed an algorithm for multimodal multiobjective optimization based on differential evolution with respect to the feature subset selection problem. We have proposed the probability initialization method to identify the selected features with equal distribution in the search space. We have also proposed a niching technique to explore the search space and exploit the nearby solutions. Further, we have proposed a convergence archive to locate and store the optimal feature subsets. Exhaustive experimentation has been conducted on different datasets with varying characteristics to identify multiple feature subsets. We have also proposed an evaluation metric for the quantitative comparison of the proposed algorithm with the existing algorithms. Results have also been compared with existing algorithms in the objective space and in terms of classification accuracy, which shows the effectiveness of the proposed algorithm. • Multimodal multiobjective feature subset selection using differential evolution is explored in this paper. • Individuals initialized using Probability Based Initialization technique. • Balanced Niching Technique used to create and balance niches. • Stagnated Archive to identify the stagnated individuals and explore search space. • Multi Set Value metric for identifying the multiple feature subsets obtained. [ABSTRACT FROM AUTHOR]

Recently, Extreme learning machine (ELM), an efficient training algorithm for single-hidden-layer feedforward neural networks (SLFN), has gained increasing popularity in machine learning communities. In this paper the ELM based Area Under the ROC Curve (AUC) optimization algorithms are studied so as to further improve the performance of ELM for imbalanced datasets. For binary class problems, a novel ELM algorithm is proposed based on an efficient least square method. For multi-class problems, the following works are done in this paper: First of all, theoretical comparison analysis is proposed for the potential multi-class extensions of AUC; Secondly, a unified objective function for multi-class AUC optimization is proposed following the theoretical analysis; Subsequently, two ELM based multi-class AUC optimization algorithms called E L M M A U C and E L M m a c r o A U C respectively are proposed followed with complexity analyses; Finally, the generalization analysis is established for E L M M A U C in search of theoretical supports. Empirical study on a variety of real-world datasets show the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Recently, there has been growing interest in social network analysis. Graph models for social network analysis are usually assumed to be a deterministic graph with fixed weights for its edges or nodes. As activities of users in online social networks are changed with time, however, this assumption is too restrictive because of uncertainty, unpredictability and the time-varying nature of such real networks. The existing network measures and network sampling algorithms for complex social networks are designed basically for deterministic binary graphs with fixed weights. This results in loss of much of the information about the behavior of the network contained in its time-varying edge weights of network, such that is not an appropriate measure or sample for unveiling the important natural properties of the original network embedded in the varying edge weights. In this paper, we suggest that using stochastic graphs, in which weights associated with the edges are random variables, can be a suitable model for complex social network. Once the network model is chosen to be stochastic graphs, every aspect of the network such as path, clique, spanning tree, network measures and sampling algorithms should be treated stochastically. In particular, the network measures should be reformulated and new network sampling algorithms must be designed to reflect the stochastic nature of the network. In this paper, we first define some network measures for stochastic graphs, and then we propose four sampling algorithms based on learning automata for stochastic graphs. In order to study the performance of the proposed sampling algorithms, several experiments are conducted on real and synthetic stochastic graphs. The performances of these algorithms are studied in terms of Kolmogorov-Smirnov D statistics, relative error, Kendall's rank correlation coefficient and relative cost. [ABSTRACT FROM AUTHOR]

Overlapping community detection is the key research work to discover and explore the social networks. A great deal of work has been devoted to detect overlapping communities, but no one can give a clear formula definition of community from the internal structure to the external boundary. More in depth, there are four challenges to existing research works. In this paper, firstly we propose overlapping community forest model and disjoint community forest model based on the community forest model, secondly give a clear formula definition of overlapping community and disjoint community based on the backbone degree and expansion, thirdly propose a novel algorithm to find overlapping communities based on the backbone degree and expansion to resolve the four challenges. This algorithm has better performance than four related algorithms mentioned by this paper in large scale social networks. It works well on American college football, Zachary’s Karate Club, Netscience-coauthor, Condensed matter collaborations, LFR etc. data sets. [ABSTRACT FROM AUTHOR]

Top- k high utility itemset mining is the process of discovering the k itemsets having the highest utilities in a transactional database. In recent years, several algorithms have been proposed for this task. However, it remains very expensive both in terms of runtime and memory consumption. The reason is that current algorithms often generate a huge amount of candidate itemsets and are unable to prune the search space effectively. In this paper, we address this issue by proposing a novel algorithm named kHMC to discover the top- k high utility itemsets more efficiently. Unlike several algorithms for top- k high utility itemset mining, kHMC discovers high utility itemsets using a single phase. Furthermore, it employs three strategies named RIU, CUD, and COV to raise its internal minimum utility threshold effectively, and thus reduce the search space. The COV strategy introduces a novel concept of coverage . The concept of coverage can be employed to prune the search space in high utility itemset mining, or to raise the threshold in top- k high utility itemset mining, as proposed in this paper. Furthermore, kHMC relies on a novel co-occurrence pruning technique named EUCPT to avoid performing costly join operations for calculating the utilities of itemsets. Moreover, a novel pruning strategy named TEP is proposed for reducing the search space. To evaluate the performance of the proposed algorithm, extensive experiments have been conducted on six datasets having various characteristics. Results show that the proposed algorithm outperforms the state-of-the-art TKO and REPT algorithms for top- k high utility itemset mining both in terms of memory consumption and runtime. [ABSTRACT FROM AUTHOR]

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html . [ABSTRACT FROM AUTHOR]

This paper aims to develop a data-driven approximate causal inference model using the newly-proposed evidential reasoning (ER) rule. The ER rule constitutes a generic conjunctive probabilistic reasoning process and generalises Dempster’s rule and Bayesian inference. The belief rule based (BRB) methodology was developed to model complicated nonlinear causal relationships between antecedent attributes and consequents on the basis of the ER algorithm and traditional IF-THEN rule-based systems, and in essence it keeps methodological consistency with Bayesian Network (BN). In this paper, we firstly introduce the ER rule and then analyse its inference patterns with respect to the bounded sum of individual support and the orthogonal sum of collective support from multiple pieces of independent evidence. Furthermore, we propose an approximate causal inference model with the kernel mechanism of data-based approximate causal modelling and optimal learning. The exploratory approximate causal inference model inherits the main strengths of BN, BRB and relevant techniques, and can potentially extend the boundaries of applying approximate causal inference to complex decision and risk analysis, system identification, fault diagnosis, etc. A numerical study on the practical pipeline leak detection problem demonstrates the applicability and capability of the proposed data-driven approximate causal inference model. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent scheme and design a spatial information sampling algorithm (SIS) to achieve a balance between exploitation and exploration more efficiently. In SIS, by creatively using a chaotic map, we divide the population of the algorithm into external and internal parts and activate the corresponding exploitation or exploration operations according to the location where the optimal individuals appear in the population. The intelligent scheme makes full use of the spatial information in the solution space where the algorithm's population is located. It also gives the algorithm the authority to start exploitation or exploration operations autonomously based on the information in the space, which increases the flexibility of the algorithm in complex optimization problems. This novel scheme not only further promotes the intelligence of the algorithm in a simple architecture but also creates a new way of balancing exploitation and exploration in the optimization process. We test the SIS on IEEE CEC2017, IEEE CEC2011, an artificial neural model training problem, and a position optimization problem of wave energy converters. When compared to other state-of-the-art meta-heuristics, the results of the Wilcoxon signed-rank test, Wilcoxon rank-sum test, and Friedman test revealed that SIS possesses superior solutions in terms of global optimality, avoidance of local minima, and solution quality. The source code of SIS is released at https://toyamaailab.github.io/. • This paper proposes a new scheme to balance the exploitation and exploration. • The new scheme enables SIS to independently perceive, analyze, and take action. • SIS uses chaotic map to construct a population to realize an intelligent scheme. • SIS has high flexibility in dealing with different optimization problems. • The simulation results show that SIS is superior to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed to address the shortcoming that a single response mechanism is suitable only for solving a certain type of dynamic multiobjective optimization problem. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D), and it is denoted as the MOEA/D-ARMS. Unlike the existing approaches, the ARMS strategy can adaptively select effective response mechanisms from the response mechanism pool based on the recent performance of each response mechanism. Four representative response mechanisms are selected to form the response mechanism pool. An overall evaluation strategy that assigns rewards to the response mechanism is adopted, and a probability-based method that is used to decide which response mechanism can be used to generate a new solution is employed. The proposed MOEA/D-ARMS algorithm is tested on two groups of test instances and compared with the decomposition-based and dominance-based DMOEAs. The results of the proposed MOEA/D-ARMS algorithm are superior to the compared algorithms, demonstrating its effectiveness. • This paper proposed an adaptive response mechanism selection (ARMS) framework and a dynamic MOEA. • The ARMS framework can adaptively select the most effective response mechanisms based on their recent performance. • An overall evaluation strategy that assigned rewards to the response mechanism was adopted. • The simple ARMS framework can easily incorporate more response mechanisms. • The statistical results clearly demonstrated that MOEA/D-ARMS were superior to the compared algorithms. [ABSTRACT FROM AUTHOR]

This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic. First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their high effectiveness and efficiency in solving multiobjective optimization problems (MOPs). However, when solving MOPs, it is important but difficult to maintain a good balance of exploration and exploitation. In addition, some reference point based MOEAs with fixed reference points perform poorly on MOPs with irregular frontiers. Therefore, this paper proposes a new multistrategy multiobjective differential evolutionary (DE) algorithm, named RLMMDE. In RLMMDE, a multistrategy and multicrossover DE optimizer is utilized to alleviate the exploration and exploitation dilemma. An adaptive reference point activation mechanism based on RL is proposed to activate the adaptive adjustment of reference points. Moreover, a reference point adaptation method is proposed to improve the performance of RLMMDE on irregular frontier problems. Experimental results of RLMMDE tested on some benchmark test suites (i.e., ZDT, DTLZ, UF, WFG, and LSMOP) and two practical mixed-variable optimization problems show that the algorithm outperforms some advanced MOEAs. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes two stochastic variance reduced gradient algorithms based on adaptive complex-valued limited-memory BFGS (ACL-BFGS) algorithm, which are established on the calculation of Wirtinger gradient for fully complex-valued dendritic neuron model (FCDNM). Basically, the explicit expression of Wirtinger gradient is derived to enable the quasi-Newton algorithm for the training of FCDNM. A stochastic variance reduction strategy is adopted such that the number of samples per batch is increased during the optimization process to ensure the effectiveness of the stochastic algorithm. The stochastic ACL-BFGS algorithm with variance reduction (SACL-BFGS) can be efficiently applied for the training of FCDNM. Furthermore, by generalizing the A3 accumulative multi-step quasi-Newton method, the A3-SACL-BFGS algorithm is presented to achieve fast convergence. The experimental results on some benchmark prediction and classification problems conclusively imply that the proposed SACL-BFGS and A3-SACL-BFGS algorithms for FCDNM are clearly superior to complex-valued backpropagation (CBP), CL-BFGS and ACL-BFGS algorithms. [ABSTRACT FROM AUTHOR]

An efficient convolution kernel could transform images into more expressive representations, which usually determines the quality of image classification models. Algorithms in the field of hyper-parameter optimization and neural architecture search could be applied to mining optimal kernels, while hyper-parameter optimization algorithms always require multiple training sessions to evaluate hyper-parameter configurations, resulting in a high time consumption. Recent gradient-based architecture search algorithms could deal with such problems within a single training session. However, there are currently no gradient-based NAS algorithms that could be used to efficiently optimize convolution kernels. This paper proposes a novel gradient-based algorithm, SoftStep, which could precisely and efficiently fine-tune the hyper-parameters of kernels. The proposed SoftStep is applied for the optimization of the convolution kernel's key hyper-parameters and generalized to more hyper-parameters in deep learning. In experiments, architecture search tasks on multiple challenging datasets are compared to various state-of-art hyper-parameter optimization and architecture search algorithms. The reported results show that SoftStep could mine optimal kernels far more efficiently than other SOTA methods. [ABSTRACT FROM AUTHOR]

With a small scale of prior information, including pairwise constraints and partial labels, semi-supervised clustering algorithms can improve clustering performance by imposing certain restrictions on the clustering results, and have accordingly attracted a lot of attention in recent years. Moreover, a large number of graph-based semi-supervised clustering algorithms have been proposed, since graphs are naturally suited to pairwise constraints. However, most graph-based approaches involve a two-step process and are not scalable to large-scale datasets due to their high time complexity. Additionally, they also encounter a serious problem of constraint violation. To deal with this issue, we propose a new non-parametric model to directly obtain the indicator matrix by minimizing the cut of the graph with a regularization term that corresponds to the balance of the clusters. To simultaneously support the two forms of prior information, our approach restricts the indicator matrix during optimization, while also ensuring that the clusters obtained by our approach do not violate any constraints. The computational complexity of the proposed approach is nearly linear to the data size n. Experiments show that the algorithm proposed in this paper achieves significant improvements in both clustering performance and efficiency. [ABSTRACT FROM AUTHOR]

Multi-scenario multi-objective optimization problems (MSMOPs) are a topic of considerable interest in the field of optimization. The MSMOP contains a set of multi-objective optimization problems arising from varying operating conditions, with the goal of determining a set of communal compromise solutions. However, there are few universal methods available for MSMOPs. This paper presents a general method incorporating transfer learning for MSMOPs. First, a multi-scenario ensemble framework that transfers knowledge between scenarios is developed to combine arbitrary multi-objective evolutionary algorithms, where a scenario-based comprehensive evaluation indicator is developed for combination with base learners. Then, an adaptive decomposition-based multi-objective evolutionary algorithm with a bi-layer selection (EADaBS) is proposed and embedded within the framework as a base learner. EADaBS incorporates an adaptive fitness assignment in its first layer to facilitate exploration, and density measurement in its second layer to ensure exploitation. A rebalancing operator is also designed to aid the population towards the Pareto front. Finally, a multitude of experiments is conducted to verify the effectiveness and efficiency of the proposed algorithms. Two three-scenario multi-objective optimization problems are designed and utilized as test problems. The experimental results clearly demonstrate that the proposed framework outperforms existing state-of-the-art algorithms. In conclusion, this research provides new insights into the solution of MSMOPs. [ABSTRACT FROM AUTHOR]

The segmentation of tunnel boring machine (TBM) time series plays a crucial role in analyzing TBM operating statuses and mining potential information from the collected signals. In this paper, a novel algorithm named SDP–RLR is proposed to segment multivariate TBM time series with outliers caused by harsh operating environments and changeable tunneling statuses. In this algorithm, the 3 σ rule is extended to externally studentized residuals of a linear regression and used to identify the outliers in each segment of the input time series. An outlier removal penalty term is added to the segment errors to avoid regarding borderline data points as outliers. As a segmentation optimization algorithm, dynamic programming (DP) is improved to scalable dynamic programming (SDP) in this paper by combining time series in two stages to reduce computational costs. In the first stage, consecutive time points are integrated to reduce the calculations required for DP, while the second stage refines the segmentation results obtained from the first stage. Experiments are conducted on synthetic datasets to evaluate the performance of the SDP–RLR algorithm, and a complete TBM time series in terms of three key variables validates its effectiveness for segmentation and outlier detection tasks. The SDP–RLR algorithm can segment a TBM time series into four different statuses, which assists in analyzing the operating statuses of TBMs. In addition, comparative experiments indicate that the proposed algorithm can handle the segmentation of complex TBM time series when they cannot be successfully processed using the segmentation approach after outlier detection. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the problem of wrapper feature subset selection (FSS) in classification-oriented datasets with a (very) large number of attributes. In high-dimensional datasets with thousands of variables, wrapper FSS becomes a laborious computational process because of the amount of CPU time it requires. In this paper we study how under certain circumstances the wrapper FSS process can be speeded up by embedding the classifier into the wrapper algorithm, instead of dealing with it as a black-box. Our proposal is based on the combination of the NB classifier (which is known to be largely beneficial for FSS) with incremental wrapper FSS algorithms. The merit of this approach is analyzed both theoretically and experimentally, and the results show an impressive speed-up for the embedded FSS process. [Copyright &y& Elsevier]

Highlights: [•] This paper proposes a decomposition algorithm for sparse signal reconstruction. [•] A small quadratic programming problem is solved in each iteration. [•] The convergence of the decomposition algorithm is also shown in this paper. [•] The decomposition method can get a fast convergence for small regularization values. [Copyright &y& Elsevier]

Flexible job shop scheduling problem (FJSP) has attracted many research interests, in particular for meta-heuristic algorithm (MA) developers due to the superior optimization performance. Dragonfly algorithm (DA) is one of recent and popular MA approaches. However, it is inevitable for DA to be trapped into local optima, especially when dealing with the complex large-scale flexible job shop scheduling problem (LSFJSP). In this paper, an improved DA, adopting a dynamic opposite learning (DOL) strategy, is proposed (namely DOLDA) to solve the LSFJSP. DOL strategy is embedded into the population initialization stage and the generation jumping stage to raise the search ability of DA. This paper uses a T-test to testify whether there are differences between the proposed algorithm and other comparison algorithms, comparison results indicate that DOLDA shows noticeable differences with other algorithms that explain the effectiveness and innovation of the proposed algorithm. The jump rate is an important parameter that determines the probability of algorithms escaping from the algorithms' local optimal solution. This paper also considers the jump rate analysis experiments to maximize the power of the DOLDA. 28 test functions from CEC 2013 and CEC 2014 are applied to verify the performance of DOLDA, test results reveal that DOLDA owns strong search ability in coping with almost all test functions. The DOLDA also is applied to solve 15 LSFJSP instances generated by Brandimate rule, the results obtained show that DOLDA can efficiently achieve a better solution on the LSFJSP compare to compared algorithms. [ABSTRACT FROM AUTHOR]

Decision-making is one of the important deals in knowledge transfer and it can be useful for the multi-source domains. Meanwhile, the existing knowledge transfer schemes do not use privacy-preserving techniques for preserving security. This can be a problem for critical domains like financial market forecasting as the misuse of security can lead to legal and financial implications. In recent years, cloud services have revolutionized various technological applications. Cloud computing has become more popular with digital technologies as it provides uninterrupted services like transmission, storage, and intensive computing of data. The architecture of the cloud is also cost-efficient. Besides, various promising services from the cloud, some challenges need to be addressed to secure the privacy of the cloud users as millions of users access its services. Privacy preservation is an important aspect in the field of data mining, and the necessity of securing important data in the cloud from hackers is on the rise. Privacy-preserving data mining algorithms have been analyzed over recent years to provide sufficient solutions for securing the privacy of the data in the cloud. This paper plans to introduce a new hybrid meta-heuristic concept for developing a privacy preservation strategy towards business data under the cloud sector. The main objective of this paper is to design a new hybrid red deer-bird swarm algorithm (RD-BSA) to ensure higher convergence and since the use of control parameters over the solution generation is minimized. The proposed privacy preservation scheme on three financial databases is evaluated with the performance against the existing privacy preservation schemes. Different analyses like statistical, key sensitivity, Known-Plaintext Attack (KPA), and Chosen-Plaintext Attack (CPA) are used for evaluating the efficiency of the algorithm. The comparative analysis of the proposed model over the conventional models demonstrates its effective performance via diverse analysis. [ABSTRACT FROM AUTHOR]

The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents' insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. This paper introduces a Dynamic Lévy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase. To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Lévy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019). Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems. [ABSTRACT FROM AUTHOR]

Multi-Agent Path Finding (MAPF) aims to find a set of conflict-free paths for multiple agents on a given graph from parking locations to specified goal locations while optimizing related costs. Currently, existing MAPF studies often consider the simplified problem setup where each agent does not return to its parking location after completing its task on the underlying graph with uniform edge costs. Nevertheless, within some real-word scenarios such as the Unmanned Aircraft System (UAS), agents are situated in the underlying graph with non-uniform edge costs. These agents are required to travel from their respective parking locations to complete tasks, and then return without conflicts. Therefore, this paper explores a new version of MAPF, formally called Multi-Agent Path Finding with Heterogeneous edges and Roundtrips (MAPF-HR). In this version, all agents are engaged in completing tasks by navigating their respective conflict-free paths with roundtrips on the graph with heterogeneous edges. This paper investigates a novel algorithm for this problem, called Improved Conflict-Based Search (CBS) with Helpful Bypass (ICBS-HB), which improves the CBS framework by utilizing the scheme of bypassing conflicts during path finding execution. With extensive experiments on MAPF benchmark maps, it shows that ICBS-HB outperforms the state-of-the-art algorithms for dealing with MAPF-HR. [ABSTRACT FROM AUTHOR]

Abstract: Automatic text classification in text mining is a critical technique to manage huge collections of documents. However, most existing document classification algorithms are easily affected by ambiguous terms. The ability to disambiguate for a classifier is thus as important as the ability to classify accurately. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to conceptualize documents into a more abstract form of concepts, and use these as the training examples to alleviate the arbitrary outcomes caused by ambiguous terms. The proposed model is evaluated on a benchmark testbed and two opinion polarity datasets. The experimental results indicate superior performance in all datasets. Applying concept analysis to opinion polarity classification is a leading endeavor in the disambiguation of Web 2.0 contents, and the approach presented in this paper offers significant improvements on current methods. The results of the proposed model reveal its ability to decrease the sensitivity to noise, as well as its adaptability in cross domain applications. [Copyright &y& Elsevier]

Abstract: Many real data sets in databases may vary dynamically. With the rapid development of data processing tools, databases increase quickly not only in rows (objects) but also in columns (attributes) nowadays. This phenomena occurs in several fields including image processing, gene sequencing and risk prediction in management. Rough set theory has been conceived as a valid mathematical tool to analyze various types of data. A key problem in rough set theory is executing attribute reduction for a data set. This paper focuses on attribute reduction for data sets with dynamically-increasing attributes. Information entropy is a common measure of uncertainty and has been widely used to construct attribute reduction algorithms. Based on three representative entropies, this paper develops a dimension incremental strategy for redcut computation. When an attribute set is added to a decision table, the developed algorithm can find a new reduct in a much shorter time. Experiments on six data sets downloaded from UCI show that, compared with the traditional non-incremental reduction algorithm, the developed algorithm is effective and efficient. [Copyright &y& Elsevier]

Abstract: Centroids are key components in many data analysis algorithms such as clustering or microaggregation. They are considered as the central value that minimises the distance to all the objects in a dataset or cluster. Methods for centroid construction are mainly devoted to datasets with numerical and categorical attributes, focusing on the numerical and distributional properties of data. Textual attributes, on the contrary, consist of term lists referring to concepts with a specific semantic content (i.e., meaning), which cannot be evaluated by means of classical numerical operators. Hence, the centroid of a dataset with textual attributes should be the term that minimises the semantic distance against the members of the set. Semantically-grounded methods aiming to construct centroids for datasets with textual attributes are scarce and, as it will be discussed in this paper, they are hampered by their limited semantic analysis of data. In this paper, we propose a method that, exploiting the knowledge provided by background ontologies (like WordNet), is able to construct the centroid of multivariate datasets described by means of textual attributes. Special efforts have been put in the minimisation of the semantic distance between the centroid and the input data. As a result, our method is able to provide optimal centroids (i.e., those that minimise the distance to all the objects in the dataset) according to the exploited background ontology and a semantic similarity measure. Our proposal has been evaluated by means of a real dataset consisting on short textual answers provided by visitors of a natural park. Results show that our centroids retain the semantic content of the input data better than related works. [Copyright &y& Elsevier]

Abstract: Selecting the best sequence of shots for a given cue position is not an easy task in a game of billiards. The repositioning of the cue after its collision with an object ball determines its success on successive shots. A previous paper by the author was able to assist users in order to perfect a shot based on a selection criterion of maximum angle tolerance. This paper further extends the aiming capability to include a calculation of the ideal speed for the repositioning of the cue ball. The system makes use of a vision system for cue and object balls, and cue stick tracking. Users are able to adjust the cue stick in terms of both the aiming direction and hitting velocity according to the guidance information analyzed by a gaming strategy of this work. A new strategy is proposed to apply the maximum tolerance angle search sequentially twice. One on the pre-collision shot and the second on the post collision path. Additional to the maximum tolerance angle criterion, this paper also proposes a new visible object ball count criterion to assist cue ball repositioning strategy for both direct and indirect shots. This criterion was developed based on an analysis of the zero tolerance zone angle. It has been specifically tested to verify its relation with the successive sink rate using proposed guidance system. The experimental results of the maximum tolerance angle repositioning strategy of our training facility as tested by users with different skill levels all out performed the results without the advice for the same set of users. In addition, the distribution pattern of maximum tolerance test showed the highest degree of similarity with that of accessibility count as user skill level increases. This not only proves the reliability of our training system, but also proves the effectiveness of our algorithm for optimal repositioning. [Copyright &y& Elsevier]

Abstract: In order to conduct classification analysis in inconsistent ordered information systems, notions on possible and compatible distribution reductions are proposed in this paper. The judgement theorems and discernibility matrices associated with the two reductions are examined, from which we can obtain an approach to the two reductions in rough set theory. Furthermore, the dominance matrix, possible and compatible decision distribution matrices are also considered for approach to these two forms of reductions in inconsistent ordered information systems. Algorithms of matrix computation for possible and compatible distribution reductions are constructed, by which we can provide another efficient approach to these two forms of distribution reductions. To interpret and help understand the algorithm, an experimental computing program is designed and two cases are employed as case study. Results of the small-scale case are calculated and compared by the discernibility matrix and the matrix computation to verify the new method we study in this paper. The large-scale case are calculated by the experimental computing program and validated by the definition of the reductions. [Copyright &y& Elsevier]

Abstract: In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling utilizing the Latent Dirichlet Allocation (LDA). Fraudulent behavior is detected with use of a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The paper provides also a method for automatic threshold computation. The accounts are classified with use of the Kullback–Leibler divergence (KL-divergence). Therefore, we also introduce three methods for approximating the KL-divergence between two LDAs. Finally, the results of experimental study on KL-divergence approximation and fraud detection in telecommunications are reported. [Copyright &y& Elsevier]

Web page ranking is one of the core components of search engines. Given a user query, ranking aims to provide a ranked list of Web pages that the user is likely to prefer the most. By and large, the ranking algorithms can be categorized into content-based approaches, link-based approaches, and hybrid approaches. Hybrid ranking algorithms, which exploit both the content and link information, are the most popular and extensively studied techniques. Among the hybrid algorithms, C-Rank combines content and link information in a very effective way using the concept of contribution. This algorithm is known to provide high performance in terms of both accurate and prompt responses to user queries. However, C-Rank suffers from very high costs to reflect the highly dynamic and extremely frequent changes in the World Wide Web, because it re-computes all of the C-Rank scores used for ranking from scratch to reflect the changes. As a result, C-Rank may be considered inappropriate to provide users with accurate and up-to-date search results. This paper aims to remedy this limitation of C-Rank. We propose incremental C-Rank , which is designed to update the C-Rank scores of only a carefully chosen portion of the Web pages rather than those of all of the Web pages without any accuracy loss. Our experimental results on a real-world dataset confirm both the effectiveness and efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

Today, big data processing has become a challenging task due to the amount of data collected using various sensors increasingly significantly. To build knowledge and predict the data, traditional data mining methods calculate all numerical attributes into the memory simultaneously. The data stream method is a solution for processing and calculating data. The method streams incrementally in batch form; therefore, infrastructure memory is sufficient to develop knowledge. The existing method for data stream prediction is FIMT-DD (Fast Incremental Model Tree-Drift Detection). Using this method, knowledge is developed in tree form for every instance. In this paper, enhanced FIMT-DD is proposed using ARDEV (Average Restrain Divider of Evaluation Value). ARDEV utilizes the Chernoff bound approach with error evaluation, improvement in learning rate, modification of perceptron rule calculation, and utilization of activation function. Standard FIMT-DD separates the tree formation process and perceptron prediction. The proposed method evaluates and connects the development of the tree for knowledge formation and the perceptron rule for prediction. The prediction accuracy of the proposed method is measured using MAE, RMSE and MAPE. From the experiment performed, the utilization of ARDEV enhancement shows significant improvement in terms of accuracy prediction. Statistically, the overall accuracy prediction improvement is approximately 6.99 % compared to standard FIMT-DD with a traffic dataset. [ABSTRACT FROM AUTHOR]

Abstract As a relatively new swarm intelligence algorithm, the fireworks algorithm (FWA) has been applied to solve lots of real-world optimization problem. However, there are still some shortcomings in the FWA algorithms. The search equation of FWA is relatively simple. Since the search mechanism of FWA mainly relies on the explosion sparks, the exploration and exploitation abilities of the algorithm are limited. In order to improve the performance of FWA, a simplified hybrid fireworks algorithm (SHFWA) is proposed in this paper. In SHFWA, to enhance the exploitation ability, a modified search formula is designed for core firework swarm. To enhance the exploration ability, for each firework swarm, another way of generating sparks–harmony spark is designed. In the conventional fireworks algorithm, the calculation of the number of sparks generated by each firework and the calculation of amplitude of explosion for each firework are very complex. In SHFWA, a simplified method is employed to compute these two variables. By introducing these methods, SHFWA is easy to implement and is good at exploration and exploitation. The proposed algorithm is tested on 40 benchmark functions. The experimental results demonstrate that SHFWA performs effectively and competitively when compared with several reported algorithms. [ABSTRACT FROM AUTHOR]

Abstract This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an improved discrete bacterial foraging algorithm to determine the optimal sequence of parts and robot moves in order to minimize the cycle time for the 2-machine robotic cell scheduling problem with sequence-dependent setup times. We present a method to convert the solutions from continuous to discrete form. In addition, two neighborhood search techniques are employed to updating the positions of each bacterium during chemotaxis and elimination–dispersal operations in order to accelerate the search procedure and to improve the solution. Moreover, a multi-objective optimization algorithm based on NSGA-II combined with the response surface methodology and the desirability technique is applied to tune the parameters as well as to enhance the convergence speed of the proposed algorithm. Finally, a design of experiment based on central composite design is used to determine the optimal settings of the operating parameters of the proposed algorithm. The results of the computational experimentation with a large number of randomly generated test problems demonstrate that the proposed method is relatively more effective and efficient than the state-of-the-art algorithms in minimizing the cycle time in the robotic cell scheduling. [ABSTRACT FROM AUTHOR]

Abstract A novel non-parallel hyperplane Universum support vector machine (U -NHSVM) is proposed in this paper. Universum data with ensconced prior knowledge are exploited by a non-parallel hyperplane support vector machine. In contrast to other algorithms, the proposed U -NHSVM shows flexibility by exploiting the prior knowledge ensconced in Universum and provides consistency by constructing two non-parallel hyperplanes simultaneously. With Universum, U -NHSVM is clearly effective but also time consuming. Therefore, a safe sample screening rule (SSSR) for U -NHSVM is also proposed based on its sparsity, termed SSSR- U -NHSVM. Because only the non-SVs are excluded from both labelled and Universum samples, the efficiency of SSSR- U -NHSVM is extremely improved while the accuracy is completely conserved. Numerical experiments on seventeen benchmark datasets and a Chinese wine dataset are carried out to demonstrate the validity of the proposed U -NHSVM and SSSR- U -NHSVM. [ABSTRACT FROM AUTHOR]

Abstract Spectral clustering algorithm (SC) has recently received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that the commonly used distance measures in SC cannot simultaneously consider global and local consistencies and are sensitive to various noises. As a result, the obtained similarity matrices are unable to capture the actual data structure and thus produce poor clustering results, especially when the data exhibits nonlinear and local manifold structures characteristics. In order to address those limitations, we present a spectral clustering algorithm using density-sensitive distance measure with global and local consistencies in this paper. In the presented algorithm, a novel manifold distance with exponential term and scaling factor is introduced as the pairwise similarity measure. By modifying its exponential term and scaling factor, we can flexibly adjust the ratio of the similarities between the data within the same manifold to the similarities between the data across different manifolds. This flexible adjustment is beneficial to the obtained similarity matrix more accurately reflecting the global and local consistencies of data structures. In addition, to eliminate the effect of noises on the clustering performance, we also incorporate the relative density sensitive term into the distance measure to take into account the local distribution characteristics of the data. Finally, to further improve clustering performance, we provide the SC-based k value determination method for k nearest neighbors (KNN) graph. In the experimental part, the effect of parameters on the performance of the proposed technique is discussed and some suggestions about the determination of the parameters are given. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing spectral clustering techniques with good robustness. [ABSTRACT FROM AUTHOR]

Abstract The recently proposed two-dimensional locality preserving projection (2DLPP) is an excellent matrix-based dimensionality reduction method. However, the formulation of 2DLPP may encounter several drawbacks in real-world applications, such as sensitiveness to outliers, non-orthogonal projections, and singularity problem. To alleviate these issues, in this paper, we propose a novel robust two-dimensional locality preserving projection (2DRLPP) for noisy image recognition. The proposed 2DRLPP preserves the local manifold structure of two-dimensional image space under the robust L 1 -norm criterion. Different from the existing 2DLPP, our 2DRLPP enjoys several incomparable advantages: (i) 2DRLPP extracts projections from matrix-based image space via the L 1 -norm locality preserving criterion rather than the L 2 -norm one, and hence it is more robust to outliers. (ii) The orthogonality constraint is further imposed on 2DRLPP to ensure its orthogonal projections. (iii) The introduced regularization term can not only control the model complexity but also guarantee the stability of solution. (iv) A simple but efficient iterative algorithm is presented to solve the corresponding L 1 -norm minimization problem, whose convergence is guaranteed theoretically. Extensive experimental results on three noisy face image datasets confirm the feasibility and robustness of the proposed approach. Highlights • Robust two-dimensional locality preserving projection (2DRLPP) is proposed for noisy image recognition. • 2DRLPP extracts projections from matrix space via the L 1 -norm criterion, which is robust to outliers. • The orthogonality constraint is imposed on 2DRLPP to ensure its orthogonal projections. • A regularization term is adopted to control the model complexity and guarantee the stability of solution. • An efficient iterative algorithm is presented to solve the L 1 -norm problem of 2DRLPP, whose convergence is guaranteed theoretically. [ABSTRACT FROM AUTHOR]

Abstract Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior observed experimentally for the cortical neurons in the visual cortex of a cat's brain, and the intersecting cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation paradigm abstracted from the structure and functioning of biological cells that provide models working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems (for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The Turing universality of DTNP systems as number accepting/generating devices is established. In addition, an universal DTNP system having 109 neurons for computing functions is constructed. Highlights • We propose a dynamic threshold neural P systems, inspired from intersecting cortical model. • We prove that Turing universality of dynamic threshold neural P systems as number accepting/generating devices. • We construct an universal dynamic threshold neural P system having 109 neurons for computing functions. [ABSTRACT FROM AUTHOR]

Abstract Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Abstract: Attribute reduction is an important research concept in rough set theory. Many attribute reduction algorithms were designed for the static information system in the past years. However, many real-world data are generated dynamically. Then a new dynamic attribute reduction algorithm based on a 0-1 integer programming is proposed to deal with the dynamic data in this paper. When multiple objects in the information system evolve over time, instead of treating the changed information table as a new one and finding the reduct again like rough set reduction algorithm does, the proposed algorithm just updates the original reduct. Therefore, its computational speed improves greatly. In addition, an approach of constraint preprocessing is also presented in this paper. Numerical experiments on twelve benchmark datasets testify the feasibility and validity of the proposed algorithm. [Copyright &y& Elsevier]

Abstract Many efforts have focused on studying techniques for selecting most informative features from data sets. Especially, the related family-based approaches have been provided for attribute reduction of covering information systems. However, the existing related family-based methods have to recompute reducts for dynamic covering decision information systems. In this paper, firstly, we investigate the mechanisms of updating the related families and attribute reducts by the utilization of previously learned results in dynamic covering decision information systems with variations of attributes. Then, we design incremental algorithms for attribute reduction of dynamic covering decision information systems in terms of attribute arriving and leaving using the related families and employ examples to demonstrate that how to update attribute reducts with the proposed algorithms. Finally, experimental comparisons with the non-incremental algorithms on UCI data sets illustrate that the proposed incremental algorithms are feasible and efficient to conduct attribute reduction of dynamic covering decision information systems with immigration and emigration of attributes. [ABSTRACT FROM AUTHOR]

Highlights • A multiobjective adaptive symbiotic organisms search (MOASOS) is proposed. • A two-archive approach is also applied in MOASOS to maintain population diversity. • Five benchmark planar/space trusses are employed as case studies. • The proposed algorithms are compared with similar studies published in the literature. • The results show the merits of the proposed algorithms. Abstract This paper presents a multiobjective adaptive symbiotic organisms search (MOASOS) and its two-archive technique for solving truss optimization problems. The SOS algorithm considers the symbiotic relationship among various species, such as mutualism, commensalism, and parasitism, to live in nature. The heuristic characteristics of the mutualism phase permits the search to jump into not visited sections (named an exploration) and allows a local search of visited sections (named an exploitation) of the search region. As search progresses, a good balance between an exploration and exploitation has a greater impact on the solutions. Thus, adaptive control is now incorporated to propose MOASOS. In addition, two-archive approach is applied in MOASOS to maintain population diversity which is a major issue in multiobjective meta-heuristics. For the design problems, minimization of the truss' mass and maximization of nodal displacement are objectives whereas elemental stress and discrete cross-sectional areas are assumed to be behaviour and side constraints respectively. The usefulness of these methods to solve complex problems is validated by five truss problems (i.e. 10-bar truss, 25-bar truss, 60-bar truss, 72-bar truss, and 942-bar truss) with discrete design variables. The results of the proposed algorithms have demonstrated that adaptive control is able to provide a better and competitive solutions when compared against the previous studies. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a collection of refinement operators for directed labeled graphs (DLGs), and a family of distance and similarity measures based on them. We build upon previous work on refinement operators for other representations such as feature terms and description logic models. Specifically, we present eight refinement operators for DLGs, which will allow for the adaptation of three similarity measures to DLGs: the anti-unification-based, S λ , the property-based, S π , and the weighted property-based, S wπ , similarities. We evaluate the resulting measures empirically, comparing them to existing similarity measures for structured data in the context of instance-based machine learning. [ABSTRACT FROM AUTHOR]

Abstract Addressing imbalanced datasets in classification tasks is a relevant topic in research studies. The main reason is that for standard classification algorithms, the success rate when identifying minority class instances may be adversely affected. Among different solutions to cope with this problem, data level techniques have shown a robust behavior. In this paper, the novel imbalance package is introduced. Written in R and C++, and available at CRAN repository, this library includes recent relevant oversampling algorithms to improve the quality of data in imbalanced datasets, prior to performing a learning task. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript. [ABSTRACT FROM AUTHOR]

Abstract Dimensionality reduction (DR) technique is a significant tool for feature extraction. In this paper, we propose an innovative two-dimensional (2D) algorithm for DR based on matrix regression model, termed matrix regression preserving projections (MRPP). Unlike many existing 2D algorithms, we construct weight matrix in the high-dimensional space based on nuclear norm (N-norm), which can well preserve the low-rank information of samples. Although MRPP is global in nature, it can also keep locality of data since it uses all training samples to reconstruct data. To make the distance metric more reliable and robust, we also replace F-norm with N-norm to calculate the reconstruction error in the projected space. Besides, L2,1-norm regularization is regularized on transformation matrix so that discrimination information can be learnt in the latent space. MRPP harnesses local and sparse preserving projections, and low rankness of high-dimensional data to build a graph. Therefore, it can learn a robust subspace from the corrupted data, i.e., illumination changes and occlusion. To verify the effectiveness of MRPP, we compare MRPP with a number of the state-of-the-art DR methods. Experiments on benchmark face datasets (Extend Yale B, CMU PIE, FERET, AR and LFW) demonstrate that MRPP outperforms the related state-of-the-arts with prominent results. [ABSTRACT FROM AUTHOR]