Search

Abstract: In networked data, linked objects tend to belong to the same class, and densely linked subgraphs are often available. Based on these facts, this paper presents a regularization framework that consists of fitting and regularization terms for transductive learning in networked data. The desirable value of the fitting term is related to the number of labeled data, whereas that of the regularization term is dependent on the structure of the graph. The ratio of these two desirable values is essential for the estimation of the optimal regularization parameters, such as that proposed in our paper. Under the proposed regularization framework, an effective classification algorithm is developed. Two methods are also introduced to incorporate contents of objects into the proposed framework to ultimately improve classification accuracy. Promising experimental results are reported on a toy problem and a paper classification task. [Copyright &y& Elsevier]

Abstract: Recommender systems are very useful in domains in which a large amount of continuous information needs to be evaluated before a decision is made. Systems that permanently interact with users need to be adapted to changes in their interests. This paper proposes an algorithm that takes advantage of the preference information implicit in the actions of the user to dynamically adapt the user profile, in which user preferences are represented as fuzzy sets. The algorithm has been tested with real data extracted from the New York Times and has shown promising results. This paper presents the adaptation algorithm and discusses the influence of its basic parameters. [Copyright &y& Elsevier]

The advantages and applications of rule-based systems have caused them to be widely recognized as one of the most popular systems in human decision-making, due to their accuracy and efficiency. To improve the performance of rule-based systems, there are several issues proposed to be focused. First, it is unnecessary to take the entire rule base into consideration during each decision-making process. Second, there is no need to visit the entire rule base to search for the key rules. Last, the key rules for each decision-making process should be different. This paper focuses on an advanced extended belief rule base (EBRB) system and proposes a multi-attribute search framework (MaSF) to reconstruct the relationship between rules in the EBRB to form the MaSF-based EBRB. MaSFs can be divided into k -dimensional tree (KDT)-based MaSFs and Burkhard–Keller (BKT)-based MaSFs. The former is targeted at decision-making problems with small-scale attribute datasets, while the latter is for those with large-scale attribute datasets. Based on the MaSF-based EBRB, the k -neighbor search and the best activated rule set algorithms are further proposed to find both the unique and the desired rules for each decision-making process without visiting the entire EBRB, especially when handling classification problems with large attribute datasets. Two sets of experiments based on benchmark datasets with different numbers of attributes are performed to analyze the difference between KDT-based MaSFs and BKT-based MaSFs, and to demonstrate how to use MaSFs to improve the accuracy and efficiency of EBRB systems. MaSFs and their corresponding algorithms are also regarded as a general optimization framework that can be used with other rule-based systems. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees. The behaviour of this algorithm is determined by two search strategies: an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem. [Copyright &y& Elsevier]

Abstract: This paper explores the simultaneous batch splitting and scheduling problem for identical parallel production lines, which is different from the parallel machine scheduling in that the processing time of a batch on parallel production lines is not fully proportional to that of a product. To solve the problem, a formula is derived to determine the processing time of a batch on a production line which is not taken as a single machine for each item in the batch. A lower bound is provided for the starting time of a batch production, which overlaps the previous one. A sufficient condition for guaranteeing the constant availability of the end buffer is given and proven mathematically with respect to a continuously delivered batch. Based on the above, an algorithm whose complexity is unrelated to the batch size is proposed to obtain the starting time of a batch production. Finally, a heuristic method based on genetic algorithm is constructed to solve the splitting and scheduling problems simultaneously. Numerical experiments show that this method functions well to balance the loads of the production lines. In fact, the proposed approach is significantly more effective than the existing methods for production planning and scheduling on production lines (each of which is considered as a single machine for both batch and item). [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of determining the degree of possible and necessary criticality of activities as well as determining paths in networks that have fuzzy activity durations. In such networks, activities and paths are reported in a fuzzy representation as being critical, with certain degrees of possibility and necessity, instead of being declared critical or not in a binary way. Although the problem of computing the possibility and necessity degrees of criticality for paths have been investigated in the literature, those problems for activities have not yet been addressed. Herein, an efficient algorithm that relies on a path enumeration technique is proposed to compute the possibility degrees of criticality of activities. Additionally, the proposed algorithm computes paths with maximum and minimum degrees for the necessity of criticality, which correspond to activities. Real-world project networks were used to evaluate the performance of the algorithm. [Copyright &y& Elsevier]

Abstract: Accessibility/controllability are very important concepts in the design of fuzzy logic control systems (FLCS) from both theoretical and practical perspectives. This paper proposes a systematic methodology that utilizes differential geometry/Lie Algebra, that has not been investigated in the literature, for analyzing the accessibility/controllability of TS FLCS. First, we linearize TS FLCS and show that this process, although it is straightforward, has limitations when analyzing some FLCS structures. Thus, we propose a novel algorithm by consolidating the linearization and Lie Algebra techniques that provides a systematic approach to assess accessibility/controllability. The advantage of the proposed algorithm is that in the case of nonaccessibility, those associated trajectories are found. The proposed approach is generic and does not require assumptions on FLCS’s rules or structures as is common with the few existing fuzzy systems controllability analysis methods in the literature. Moreover, we show that unlike exiting methods, our approach is not limited to small rule bases, but is also applicable for systems with large number of rules. To validate the effectiveness of the proposed method, we present several illustrative benchmark examples that will be of practical use to control engineers and can, as well, enable wider scale use of FLCS in various engineering applications. [Copyright &y& Elsevier]

Abstract: This paper presents a new feature discovery approach called FEADIS that strengthens learning algorithms with discovered features. The discovered features are formed by various mathematical functions including ceil, mod, sin, and similar. These features are constructed in an iterative manner to improve gradually its learning performance. We demonstrate FEADIS capabilities by testing different types of datasets including periodical datasets. From the results, we conclude that FEADIS increases the performance of learning algorithms in a wide range of datasets for nominal or numeric target feature. Furthermore, most of the well known classifiers without FEADIS strengthening have severe difficulty in handling datasets that have periodical functional relations between input features and target feature – a difficulty circumvented by their potential use of FEADIS. [Copyright &y& Elsevier]

Abstract: In this paper, the problems of fuzzy binary relations on fuzzy n-cell number space and their applications are investigated. Firstly, we have defined some fuzzy approximation relations on fuzzy n-cell number space, and studied their properties. Secondly, as application, we have developed an algorithmic version of classification in an imprecise or uncertain environment by using the fuzzy approximation relations. Practical examples are provided to show the application and rationality of the proposed techniques. [Copyright &y& Elsevier]

Abstract: This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter may be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. [Copyright &y& Elsevier]

Abstract: Zernike moments have been extensively used and have received much research attention in a number of fields: object recognition, image reconstruction, image segmentation, edge detection and biomedical imaging. However, computation of these moments is time consuming. Thus, we present a fast computation technique to calculate exact Zernike moments by using cascaded digital filters. The novelty of the method proposed in this paper lies in the computation of exact geometric moments directly from digital filter outputs, without the need to first compute geometric moments. The mathematical relationship between digital filter outputs and exact geometric moments is derived and then they are used in the formulation of exact Zernike moments. A comparison of the speed of performance of the proposed algorithm with other state-of-the-art alternatives shows that the proposed algorithm betters current computation time and uses less memory. [Copyright &y& Elsevier]

Abstract: Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well. [Copyright &y& Elsevier]

Abstract: In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modeling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP. [Copyright &y& Elsevier]

Abstract: Several variants of the particle swarm optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization (MO) problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that govern the convergence behavior of the algorithm to the optimal Pareto front in the objective function space. Computer simulations over benchmark MO problems have also been provided to substantiate the theoretical derivations. [Copyright &y& Elsevier]

Abstract: Increasing attention is being paid to solve constrained optimization problems (COP) frequently encountered in real-world applications. In this paper, an improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs. The constraint-handling technique is based on the simple constraint-preserving method. Velocity and position of each particle, as well as the corresponding changes, are all expressed as vectors in order to present the optimization procedure in a more intuitively comprehensible manner. The NVPSO algorithm , which uses one-dimensional search approaches to find a new feasible position on the flying trajectory of the particle when it escapes from the feasible region, has been proposed to solve COP. Experimental results showed that searching only on the flying trajectory for a feasible position influenced the diversity of the swarm and thus reduced the global search capability of the NVPSO algorithm. In order to avoid neglecting any worthy position in the feasible region and improve the optimization efficiency, a multi-dimensional search algorithm is proposed to search within a local region for a new feasible position. The local region is composed of all dimensions of the escaped particle’s parent and the current positions. Obviously, the flying trajectory of the particle is also included in this local region. The new position is not only present in the feasible region but also has a better fitness value in this local region. The performance of IVPSO is tested on 13 well-known benchmark functions. Experimental results prove that the proposed IVPSO algorithm is simple, competitive and stable. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely naı¨ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification. [ABSTRACT FROM AUTHOR]

Abstract: A considerable amount of twig pattern matching algorithms have been proposed to holistically process a twig query. Those algorithms mainly focus on twig pattern query with the AND-logic. However, there is often a need to process a twig query with the OR-predicates. Furthermore, the existing algorithms fall short in their ability to support twig query with OR-logic in fuzzy XML. To overcome this limitation, in this paper, we first introduce a novel encoding scheme to represent node information in fuzzy XML. Based on the encoding scheme, we then propose an effective algorithm for matching a twig pattern query with the AND/OR-logic in fuzzy XML. Our approach adopts a compact stack technique to process the complicated twig query consisting of both AND-logic and OR-logic. More importantly, our method eliminates re-scanning unnecessary portions of XML documents and redundant intermediate results. Finally, the experimental results demonstrate the performance advantages of our approach. [Copyright &y& Elsevier]

Abstract: In many group decision-making situations, decision makers’ preferences for alternatives are expressed in preference relations (including fuzzy preference relations and multiplicative preference relations). An important step in the process of aggregating preference relations, is to determine the importance weight of each preference relation. In this paper, we develop a number of goal programming models and quadratic programming models based on the idea of maximizing group consensus. Our models can be used to derive the importance weights of fuzzy preference relations and multiplicative preference relations. We further develop iterative algorithms for reaching acceptable levels of consensus in group decision making based on fuzzy preference relations or multiplicative preference relations. Finally, we include an illustrative example. [Copyright &y& Elsevier]

Abstract: In this paper a novel computation method is proposed to perform the common vector approach (CVA) faster than its conventional implementation in pattern recognition. While conventional CVA calculations perform the classification with respect to the distance between vectors, the new method performs the classification using scalars. A theoretical proof of the equivalence of the proposed method is provided. Next, in order to verify the numerical equivalence of the proposed computation method to the conventional (vector-based) method, numerical experiments are conducted over three different face databases, namely the AR Database, extended Yale Face Database B, and FERET Database. Since the computational gain may depend on (i) the dimension of the feature vectors, (ii) the number of feature vectors used in training, and (iii) the number of classes, the effects of these items are clearly verified via these databases. Our theoretically equivalent (but faster) method provided no difference in the classification rates despite its improved classification speed as compared to the classical implementation of CVA. The new method is found to be about 2.1–3.0 times faster than the conventional CVA implementation for the AR face database, 1.9–3.3 times faster for the extended Yale Face Database B, and 1.9–3.1 times faster for the FERET Database. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we provide a general description of how to combine a collection (not necessarily finite) of asymmetric distances in order to obtain a single one as output. To this end we introduce the notion of asymmetric distance aggregation function that generalizes the well-known one for distance spaces given by Borsik and Doboš [J. Borsik, J. Doboš, On a product of metricspaces, Math. Slovaca 31 (1981) 193–205]. Among other results, a characterization of such functions is obtained in terms of monotony and subadditivity. Finally, we relate our results to Computer Science. In particular we show that the mathematical formalism based on complexity distances, which has been introduced by Romaguera and Schellekens [S. Romaguera, M. Schellekens, Quasi-metric properties of complexity spaces, Topol. Appl. 98 (1999) 311–322] for the complexity analysis of programs and algorithms, can be obtained as a particular case of our new framework using appropriate asymmetric aggregation distance functions. [Copyright &y& Elsevier]

Abstract: In this paper, the matrix-analytic approach is applied to explore the per-stream loss behavior of the multimedia traffic under RED scheme. We constructed a ∑MAP/M/1/K queuing model for the RED mechanism with multimedia traffic which follows a continuous-time Markovian arrival process (MAP). In addition to evaluating the long-term per-stream packet drop probabilities, we examine the bursty nature of per-stream packet drops by means of conditional statistics with respect to dropped periods and the probability that the queuing system stays in the dropped period. The dropped period corresponds to having more than a certain number of packets in router buffer; non-dropped period corresponds to the opposite. These performance measures describe the quality of service provided by the router to particular multimedia traffic streams in the presence of background multimedia traffic. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the generalization performance of the multi-graph regularized semi-supervised classification algorithm associated with the hinge loss. We provide estimates for the excess misclassification error of multi-graph regularized classifiers and show the relations between the generalization performance and the structural invariants of data graphs. Experiments performed on real database demonstrate the effectiveness of our theoretical analysis. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a batch fuzzy learning vector quantization algorithm that attempts to solve certain problems related to the implementation of fuzzy clustering in image compression. The algorithm’s structure encompasses two basic components. First, a modified objective function of the fuzzy c-means method is reformulated and then is minimized by means of an iterative gradient-descent procedure. Second, the overall training procedure is equipped with a systematic strategy for the transition from fuzzy mode, where each training vector is assigned to more than one codebook vectors, to crisp mode, where each training vector is assigned to only one codebook vector. The algorithm is fast and easy to implement. Finally, the simulation results show that the method is efficient and appears to be insensitive to the selection of the fuzziness parameter. [Copyright &y& Elsevier]

Abstract: Model checkers for systems represented by labelled transition systems are not as extensively used as those for systems represented by Kripke structures. This is partially due to the lack of an elegant formal language for property specification which would not be as raw as, for example, HML yet also not as complex as, for example, -calculus. This paper proposes a new action-based propositional branching-time temporal logic ACTLW, which enhances popular computation tree logic (CTL) with the notion of actions in a similar but more comprehensive way than action-based CTL introduced by De Nicola and Vaandrager [R. De Nicola, F.W. Vaandrager, Action versus logics for transition systems, in: Semantics of Systems of Concurrent Processes, Proceedings LITP Spring School on Theoretical Computer Science, LNCS 469, 1990, pp. 407–419]. ACTLW is defined by using temporal operators until and unless only, whereas all other temporal operators are derived from them. Fixed-point characterisation of the operators together with symbolic algorithms for global model checking are shown. Usage of this new logic is illustrated by an example of verification of mutual-exclusion algorithms. [Copyright &y& Elsevier]

A pair of Mond­Weir type second order symmetric nondifferentiable multiobjective programs is formulated. Weak, strong and converse duality theorems are established under Β-pseudobonvexity assumptions. Special cases are discussed to show that this paper extends some work appeared in this area. [ABSTRACT FROM AUTHOR]

The thickness of a graph is the minimum number of planar subgraphs into which the graph can be decomposed. Determining the thickness of a given graph is known to be an NP-complete problem. In this paper we introduce a new heuristic algorithm for determining the thickness of a graph. Our algorithm is based on the simulated annealing optimization scheme. We compare the quality of the solutions and running times of our algorithm against previously tested heuristic algorithms. We show that the simulated annealing is a last and efficient method to obtain good approximations for the thickness of a graph. We also give a new upper bound for the thickness of complete tripartite graphs, whose vertex sets are of equal size. [ABSTRACT FROM AUTHOR]