Search

In surface defect detection, some regions remain ambiguous and cannot be distinctly classified as abnormal or normal. This challenge is exacerbated by subjective factors, including workers' emotional fluctuations and judgment variability, resulting in noisy labels that lead to false positives and missed detections. Current methods depend on additional labels, such as clean and multi-labels, which are both time-consuming and labor-intensive. To address this, we utilize Rough Set theory and Bayesian neural networks to learn a trustworthy model from noisy labels for Surface Defect Detection. Our approach features a novel pixel-level representation of suspicious areas using lower and upper approximations, and a novel loss function that emphasizes both precision and recall. The Pluggable Spatially Bayesian Module (PSBM) we developed enhances probabilistic segmentation, effectively capturing uncertainty without requiring extra labels or architectural modifications. Additionally, we have devised a 'defect discrimination confidence' metric to better quantify uncertainty and assist in product quality grading. Without the need for extra labeling, our method significantly outperforms state-of-the-art techniques across three types of datasets and enhances seven types of classic networks as a pluggable module, without compromising real-time computing performance. For further details and implementation, our code is accessible at https://github.com/ntongzhi/RoughSet-BNNs. • A Rough Set-based method was proposed for noise label handling in defect detection. • Enhances robustness to noisy data without extra labels or network changes. • Experiments across datasets and models prove the method's effectiveness and utility. [ABSTRACT FROM AUTHOR]

As an extension of partition, fuzzy β -covering can provide a more realistic and accurate description of incomplete information. In this paper, we mainly integrate the idea of fuzzy β -covering with dual hesitant fuzzy (DHF) information and construct some novel three-way decision (3WD) models with DHF covering-based rough set. Firstly, we propose the notions of DHF β -covering, DHF β -minimal description and DHF β -maximal description, and then construct three types of DHF neighborhood operators. Meanwhile, we introduce DHF conditional probability by using DHF neighborhood operator. Secondly, in terms of DHF conditional probability, DHF covering-based probabilistic rough set, DHF covering-based decision-theoretic rough set and their 3WD models are established. In light of DHF neighborhood operator, we propose two pairs of optimistic and pessimistic DHF decision evaluation functions and build two 3WD models through them. Lastly, a numerical example is employed to elaborate the application of the above models, which is effective and credible to medicine screening for Alzheimer's disease. • Proposed neighborhood operators in dual hesitant fuzzy environment. • Three-way decision based on dual hesitant fuzzy probabilistic rough sets. • Three-way decision based on dual hesitant fuzzy decision-theoretic rough sets. • Three-way decision based on dual hesitant fuzzy decision evaluation functions. • Experiment on medication screening of Alzheimer's disease are given. [ABSTRACT FROM AUTHOR]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [ABSTRACT FROM AUTHOR]

Attribute reduction is a fundamental problem in rough set theory and serves as an effective data reduction technique. The minimal test cost attribute reduction problem poses a more challenging task built upon the classical reduction problem and finds widespread applications in the real world. The development of efficient heuristic algorithms is crucial for effectively addressing this problem. However, research efforts utilizing local search algorithms for solving the minimal test cost attribute reduction problem remain limited. This paper aims to combine the graph-based rough set attribute reduction method with the effectiveness of local search algorithms known for solving the set covering problem. It proposes a method to transform the minimal test cost attribute reduction problem into a set covering problem and a four-stage branch local search algorithm based on the set covering is designed to solve the minimal test cost attribute reduction problem, which includes three core algorithmic techniques to further improve the practical performance of the algorithm. Extensive experiments are conducted to evaluate the effectiveness of the proposed algorithms, comparing them against various existing algorithms. The experimental results demonstrate that the proposed algorithms achieve fewer attributes and lower test costs within an acceptable running time, while maintaining competitive classification accuracy. • We propose the equivalence between minimal test cost attribute reduction problem (MTCARP) and set covering problem (SCP). • We propose two algorithms for MTCARP (GRF-NuSC1 and GRF-NuSC2). • GRF-NuSC1 and GRF-NuSC2 can efficiently transform the MTCARP into the SCP. • GRF-NuSC2 employs unique stage settings and a branching strategy. • GRF-NuSC2 surpasses most of existing algorithms in achieving the minimal test cost. [ABSTRACT FROM AUTHOR]

Abstract In order to predict the service life of large centrifugal compressor impeller correctly, the rough set and fuzzy Bandelet neural network are combined to construct the novel prediction model which can give full play to theirs advantages. The attribute reduction algorithm based rough set and clustering method is firstly designed to optimize the inputting variables of fuzzy Bandelet neural network. And then the prediction model based on fuzzy Bandelet neural network is proposed, the Bandelet function is used as the excitation function of hidden layer and is combined with fuzzy theory to improve the prediction effectiveness of the prediction model. The training algorithm of fuzzy Bandelet neural network is designed based on improved genetic algorithm, the improved genetic algorithm introduces the adaptive differential evolution method into the traditional genetic algorithm, which can effectively optimize the parameters of fuzzy Bandelet neural network. Finally, the original 30 input variables of fuzzy Bandelet neural network are reduced to 9 input nodes based on rough set using 500 remanufacturing impellers as research objects. The service life of remanufacturing impeller is predicted based on three prediction models, and simulation results show that the fuzzy Bandelet neural network optimized by improved genetic algorithm has highest prediction precision and efficiency, which can correctly predict the service life of remanufacturing impeller. Highlights • Fuzzy Bandelet neural network is constructed based on Bandelet transform. • Service life prediction model is established by rough set and fuzzy Bandelet neural network. • Prediction effectiveness of service life of remanufactured impeller are improved. [ABSTRACT FROM AUTHOR]

Among various tasks of feature reduction, how to search qualified features and then construct the corresponding reduct which satisfies a given constraint is a hot topic. Constraint is the pre-defined condition in feature reduction, it rules two crucial aspects: 1) when the process of searching can be terminated to construct reduct; 2) whether a feature/set of features should be added into reduct pool or not. Obviously, such a task of feature reduction is constraint-dependent. However, some limitations may naturally emerge: 1) constraint-independent features are ignored; 2) fixed constraint hinders the diverse evaluations of features; 3) selected features based on single constraint are powerless to data perturbation. Therefore, an Ensembler Mixed with Pareto Optimality (EmPo) is developed in this study. Firstly, the principle of Restricted Pareto Optimality is proposed to identify constraint-independent features before performing constraint related searching, which indicates a two-stages strategy to feature reduction. Naturally, diverse evaluations of features are achieved in such a process. Secondly, a data perturbation w.r.t. either sample or feature aspect is employed to obtain multiple reductions of features. The objective is to further improve the stability of the classification results based on those multiple reducts. It should also be emphasized that EmPo is a general framework, and most existing approaches to feature reduction can be embedded into it to further improve their performances. Finally, the effectiveness of our EmPo is validated over 20 datasets within 4 different ratios of noisy label. • Propose a framework for selecting qualified features. • Investigate the shortcomings of some existing methods. • The quality of features is related to evaluation conditions. • Ensemble strategy is useful in selecting features. [ABSTRACT FROM AUTHOR]

Accurately forecasting power load is essential for utilities to effectively manage their resources, reduce operational costs, and provide improved customer service. However, the current load forecasting lacks the ability to deeply explore data, thus failing to accurately predict both short-term trends and volatility ranges. To address this issue, we construct a novel combined forecasting system based on rough sets, information granulation, deep learning, and multi-objective optimization. In this study, we follow the reasonable granulation criterion for granular computing, which aims to improve the reasonableness and specificity of granular interval prediction under the determination of granularity level, and innovatively propose a novel multi-objective optimization algorithm that can simultaneously constrain the reasonable granulation criterion and theoretically demonstrate the obtained Pareto-optimal solution. Four simulation experiments were conducted using the Australian dataset to evaluate the performance of our proposed system in predicting trend changes and fluctuation ranges of power load. Our results demonstrate that the developed system effectively predicts the trend changes and fluctuation range of power load. Specifically, our system showed a deterministic prediction performance improvement of 13.39% and a granularity interval prediction performance improvement of 6.67% compared to the baseline model. Moreover, we conducted a series of discussion tests to validate the superiority of our system, which further confirmed the effectiveness of our proposed approach. • A multivariate intelligent granularity combination forecasting system (MGCFS) is constructed. • A novel clustering algorithm based on intelligent optimization is presented for information granulation. • Pareto optimal solution is proved theoretically. • A multi- objective swarm intelligent optimization technique is proposed. [ABSTRACT FROM AUTHOR]

Knowledge reduction is an essential issue in data mining. However, most of the existing studies consider the problem of knowledge reduction in a single-scale system. When the system expands to a multi-scale information system (MIS), the existing methods are not available. To overcome this challenge, we design a universal framework to reduce attributes and select optimal scales (OSs) based on the dominance relation in ordered information systems (OISs). Firstly, we analyze the effect of scale on rough approximations and correlation measures in multi-scale ordered information systems (MOISs). Secondly, the relation between attribute reduction and OS selection is explored in OISs. Moreover, the corresponding algorithms are designed around both tasks. Due to our universal framework considering the decisions at the finest, optimal and coarsest scale, our study is an extension of the three-way decision. Thirdly, a novel MOIS construction algorithm is explored to transform an OIS into an MIS. Thus, the proposed reduction algorithms can be operated in any OIS. Experiments substantiate that models trained by data at the OS can improve their stability and the attribute reduction task is also suitable for the proposed algorithm. • A universal framework is explored to reduce attributes and select optimal scales. • A novel multi-scale ordered information system construction algorithm is designed. • The influence of scale on rough approximations and correlation measures is also discussed. [ABSTRACT FROM AUTHOR]

Feature selection has been a significant task for data mining and pattern recognition. It aims to choose the optimal feature subset with the minimum redundancy and the maximum discriminating ability. This paper analyzes the feature selection method from two aspects of data and algorithm. In order to deal with the redundant features and irrelevant features in high-dimensional & low-sample data and low-dimensional & high-sample data, the feature selection algorithm model based on the granular information is presented in this paper. Thus, our research examines experimentally how granularity level affects both the classification accuracy and the size of feature subset for feature selection. First of all, the improved binary genetic algorithm with feature granulation (IBGAFG) is used to select the significant features. Then, the improved neighborhood rough set with sample granulation (INRSG) is proposed under different granular radius, which further improves the quality of the feature subset. Finally, in order to find out the optimal granular radius, granularity λ optimization based on genetic algorithm (ROGA) is presented. The optimal granularity parameters are found adaptively according to the feedback of classification accuracy. The performance of the proposed algorithms is tested upon eleven publicly available data sets and is compared with other supervisory methods or evolutionary algorithms. Additionally, the ROGA algorithm is applied to the enterprise financial dataset, which can select the features that affect the financial status. Experiment results demonstrate that the approaches are efficient and can provide higher classification accuracy using granular information. [ABSTRACT FROM AUTHOR]

The minimum vertex cover problem (MVCP) and minimum weighted vertex cover problem (MWVCP) have been used in a variety of applications. This paper focuses on a view of test-cost-sensitive rough set for MWVCP. We first provide a method to convert a minimum weight vertex cover of a graph into a minimal test cost attribute reduct of a test-cost-sensitive decision table. Then, an induced test-cost-sensitive decision table from an undirected weighted graph is established. On the foundation of the induced decision table, an improved heuristic algorithm for finding minimum weight vertex covers is proposed, it can avoid a mass of redundant computation. Furthermore, to improve efficiency, a quantum-behaved particle swarm optimization with immune mechanism is presented, which can avoid the phenomenon of premature, improve the global searching ability, and enhance the convergence speed. The results of the experiment show the advantages and limitations of the proposed algorithms compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Fuzzy time series forecasting is an emergent research topic. In fuzzy time series model design, accuracy of forecast is dependent on two major issues: (1) Efficient data partitioning (2) Establishing Fuzzy logical relationships for Prediction. In this study, a new data partitioning technique based on rough-fuzzy approach has been proposed. Then, for the prediction purpose, a novel rule selection criterion has been adopted. In addition to that a mechanism is devised to deal with the situation when there is no matching rule present in the training data. Motivation for the present work is to overcome the drawback of existing high-order fuzzy time series models by avoiding the computations of complicated fuzzy logical relationship considering all previous states at a time and then explicit matching of those rules. The proposed work produces output of improved accuracy with selective rules only. In this high order model fuzzy logical relationships for each time lag are established separately and predictions are combined at the end to produce final result. Performance of the model is evaluated using TAIEX dataset. This idea also outperforms the some of the recent fuzzy time series forecasting models using the same dataset, in terms of forecast accuracy. [ABSTRACT FROM AUTHOR]

To improve the prediction capacity of stock price trend, an integrated prediction method is proposed based on Rough Set (RS) and Wavelet Neural Network (WNN). RS is firstly introduced to reduce the feature dimensions of stock price trend. On this basis, RS is used again to determine the structure of WNN, and to obtain the prediction model of stock price trend. Finally, the model is applied to prediction of stock price trend. The simulation results indicate that, through RS attribute reduction, the structure of WNN prediction model can be simplified significantly with the improvement of model performance. The directional symmetry values of prediction, corresponding to SSE Composite Index, CSI 300 Index, All Ordinaries Index, Nikkei 225 Index and Dow Jones Index, are 65.75%, 66.37%, 65.97%, 65.52% and 66.75%, respectively. The prediction results are better than those obtained by other neural networks, SVM, WNN and RS-WNN, which verifies the feasibility and effectiveness of the proposed method of predicting stock price trend. [ABSTRACT FROM AUTHOR]

Rough K-Means (RKM) is a well-known unsupervised clustering algorithm based on rough set logic that is utilized in a wide range of applications. However, when dealing with complex problems like image segmentation, it is frequently trapped in local optima during execution, resulting in undesirable segmentation results. To handle the issue in a realistic computing time, this study develops a Lévy–Cauchy Arithmetic Optimization Algorithm (LCAOA), an enhanced form of AOA, for performing rough clustering. The Levy flight and Cauchy distribution help in exploration and exploitation, respectively, in the proposed LCAOA. Therefore, well-balanced exploration and exploitation have been incorporated into LCAOA, which is a major problem of classical AOA. Opposition-based learning is also incorporated into LCAOA to maintain an efficient population during the optimization process. As the segmentation efficacy is somewhat dependent on the selection of color spaces caused by the non-illumination of regions, the suggested method employs the CIELab color space. The suggested method is compared to conventional and Nature-Inspired Optimization Algorithms (NIOA)-based state-of-the-art image segmentation techniques over traditional color images, color pathology images, and leaf images. The proposed clustering methodology outperforms all other examined clustering algorithms, according to the results of the experiments. For example, proposed LCAOA-based rough clustering gives average Feature Similarity Index (FSIM) values of 0.9513, 0.9688, and 0.9769 for traditional color images with 4, 6, and 8 clusters, respectively. The proposed technique is associated with an average FSIM value of 0.9525 for cluster number 2 in images of oral pathology. Lastly, for leaf images, the proposed approach yields a mean FSIM value of 0.9759 with an accuracy of greater than 97% for cluster number 2. • Arithmetic Optimization Algorithm (AOA) based rough clustering has been introduced. • Enhancement of AOA is performed by Lévy–Cauchy based mutation. • Opposition based learning is also incorporated into AOA. • The proposed clustering method uses CIELab color space. • The proposed method is compared against state-of-art segmentation methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationships among rough sets, soft sets and hemirings. The concept of soft rough hemirings is introduced, which is an extended notion of a rough hemiring. It is pointed out that in this paper, we first apply soft rough sets to algebraic structure-hemirings. Further, we first put forward the concepts of C -soft sets and CC -soft sets, which provide a new research idea for soft rough algebraic research. Moreover, we study roughness in hemirings with respect to MSR -approximation spaces. Some new soft rough operations over hemirings are explored. In particular, lower and upper MSR -hemirings ( k -ideal and h -ideal) are investigated. Finally, we put forth an approach for multicriteria group decision making problem based on modified soft rough sets and offer an actual example. [ABSTRACT FROM AUTHOR]

The pandemic outbreak of severe acute respiratory syndrome caused by the Coronavirus 2 disease in 2019, also known as SARS-COV-2 and COVID-19, has claimed over 5.6 million lives till now. The highly infectious nature of the Covid-19 virus has resulted into multiple massive upsurges in counts of new infections termed as 'waves.' These waves consist of numerous rising and falling counts of Covid-19 infection cases with changing dates that confuse analysts and researchers. Due to this confusion, the detection of emergence or drop of Covid waves is currently a subject of intensive research. Hence, we propose an algorithmic framework to forecast the upcoming details of Covid-19 infection waves for a region. The framework consists of a displaced double moving average (δ DMA) algorithm for forecasting the start, rise, fall, and end of a Covid-19 wave. The forecast is generated by detection of potential dates with specific counts called 'markers.' This detection of markers is guided by decision rules generated through rough set theory. We also propose a novel 'corrected moving average' (χ SMA) technique to forecast the upcoming count of new infections in a region. We implement our proposed framework on a database of Covid-19 infection specifics fetched from 12 countries, namely: Argentina, Colombia, New Zealand, Australia, Cuba, Jamaica, Belgium, Croatia, Libya, Kenya, Iran, and Myanmar. The database consists of day-wise time series of new and total infection counts from the date of first case till 31st January 2022 in each of the countries mentioned above. The δ DMA algorithm outperforms other baseline techniques in forecasting the rise and fall of Covid-19 waves with a forecast precision of 94.08%. The χ SMA algorithm also surpasses its counterparts in predicting the counts of new Covid-19 infections for the next day with the least mean absolute percentage error (MAPE) of 36.65%. Our proposed framework can be deployed to forecast the upcoming trends and counts of new Covid-19 infection cases under a minimum observation window of 7 days with high accuracy. With no perceptible impact of countermeasures on the pandemic until now, these forecasts will prove supportive to the administration and medical bodies in scaling and allotment of medical infrastructure and healthcare facilities. • Computing the trend of Covid-19 infection waves for a region by a displaced double moving average (δ DMA) technique. • Filtering the 'marker' dates in a Covid-19 infection wave by using rough sets based decision rules • Forecasting the upcoming count of new infections in a region by a novel 'corrected moving average' (χ SMA) technique. • Implementing the proposed techniques as an algorithmic framework on Covid-19 infection specifics of 12 countries. [ABSTRACT FROM AUTHOR]

Research on mean–variance portfolio optimization (MVPO) has made significant progress in the application of advanced solution algorithms or multiple criteria decision-making methods to achieve superior outcomes. However, there is little specific guidance on how to approach individually preferred optimization bounded by rationality. Therefore, the main goal of this study is to extend MVPO research by incorporating an investor's preferences and rational expectations during optimizations. Using a set of equities from the Taiwan stock market, this study proposes an interactive MVPO model guided by personal risk-return preferences and bounded by rational fuzzy constraints. Furthermore, this study adopts a rule-based bipolar model to prioritize candidate equities and support the interactive optimization process. This hybrid model is an early attempt to investigate bounded rationality in MVPO, which integrates multiple rule-based decision-making with multiple objective decision-making. The obtained portfolio outperformed two benchmark indexes in 2020, suggesting the feasibility of this approach. [Display omitted] • This paper aims to resolve the mean–variance portfolio optimization problem by considering individual preferences through interactive optimization. • We introduce the concept of rational expectation as a set of adjustable fuzzy constraints for optimization. • This study adopts a rule-based bipolar model to prioritize candidate stocks with transparent financial insight. [ABSTRACT FROM AUTHOR]

Incremental feature selection is an efficient paradigm that updates an optimal feature subset from added-in data without forgetting the previously learned knowledge. Most existing studies of rough set-based incremental feature selection require scanning all added-in samples and all possible candidate features when determining a best feature. However, such a classical search strategy has to perform some redundant calculations, which increase the computing and memory space resources. To avoid the redundant calculations, we propose a novel incremental feature selection method using sample selection and feature-based accelerator. First, a feature selection framework based on discernibility score is proposed as basis for our incremental method. Second, sample selection scheme is proposed to eliminate useless samples from added-in data. This scheme ensures that only useful samples are considered in the incremental process. Third, feature-based accelerator is designed to incrementally select a best feature and simultaneously remove redundant candidate features. It is theoretically guaranteed redundant features removed earlier remain redundant and will not be reexamined during the rest of the process. Finally, our incremental feature selection algorithm is designed by a two-stage procedure including sample selection scheme and feature-based accelerator. The results of experiments validate the time efficiency of the proposed incremental algorithm, especially on datasets with numerous instances or high dimensions. • A new feature selection framework is proposed based on discernibility score. • Sample selection scheme is proposed to eliminate useless samples from added-in data. • Feature-based accelerator is designed to select informative features and simultaneously remove redundant candidate features. • A two-stage incremental feature selection algorithm is developed by integrating sample selection scheme into feature-based accelerator. • Experimental results verify the time efficiency of the proposed incremental algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hybrid model of RS and ELM methods was proposed. [•] Feature selection has been carried out without removing missing values in the data set. [•] RS+ELM model was determined as 100.00% success rate. [•] It is considered that the proposed method will be useful in similar medical practices. [ABSTRACT FROM AUTHOR]

Abstract: This study proposes a method, designated as the GRP-index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The proposed method consists of a genetic algorithm (GA) and an FRP-index method. In the FRP-index method, the conditional and decision attribute values of the instances in the dataset are fuzzified and discretized using the Fuzzy C-means (FCM) method in accordance with the cluster vectors given by the GA specifying the number of clusters per attribute. Rough set (RS) theory is then applied to determine the lower and upper approximate sets associated with each cluster of the decision attribute. The accuracy of approximation of each cluster of the decision attribute is then computed as the cardinality ratio of the lower approximate sets to the upper approximate sets. Finally, the centroids of the lower approximate sets associated with each cluster of the decision attribute are determined by computing the mean conditional and decision attribute values of all the instances within the corresponding sets. The cluster centroids and accuracy of approximation are then processed by a modified form of the PBMF-index function, designated as the RP-index function, in order to determine the optimality of the discretization/classification results. In the event that the termination criteria are not satisfied, the GA modifies the initial population of cluster vectors and the FCM, RS and RP-index function procedures are repeated. The entire process is repeated iteratively until the termination criteria are satisfied. The maximum value of the RP cluster validity index is then identified, and the corresponding cluster vector is taken as the optimal classification result. The validity of the proposed approach is confirmed by cross validation, and by comparing the classification results obtained for a typical stock market dataset with those obtained by non-supervised and pseudo-supervised classification methods. The results show that the proposed GRP-index method not only has a better discretization performance than the considered methods, but also achieves a better accuracy of approximation, and therefore provides a more reliable basis for the extraction of decision-making rules. [Copyright &y& Elsevier]

Abstract: One of the major tasks in biomedicine is the classification and prediction of biomedical data. Ensemble learning is an effective method to significantly improve the generalization ability of classification and thus have obtained more and more attentions in the biomedicine community. However, most existing techniques in ensemble learning employ all the trained component classifiers to constitute ensembles, which are sometimes unnecessarily large and can lead to extra memory costs and computational times. For improving the generalization ability and efficiency of ensemble for biomedical classification, an Ant Colony Optimization and rough set based ensemble approach is proposed in this paper. Ant Colony Optimization and rough set theory are incorporated to select a subset of all the trained component classifiers for aggregation. Experiment results show that compared with existing methods, it not only decreases the size of ensemble, but also obtains higher prediction performance. [Copyright &y& Elsevier]

Abstract: Discretization of continuous attributes is a necessary pre-requisite in deriving association rules and discovery of knowledge from databases. The derived rules are simpler and intuitively more meaningful if only a small number of attributes are used, and each attribute is discretized into a few intervals. The present research paper explores the interrelation between discretization and reduction of attributes. A method has been developed that uses Rough Set Theory and notions of Statistics to merge the two tasks into a single seamless process named dynamic discreduction. The method is tested on benchmark data sets and the results are compared with those obtained by existing state-of-the-art techniques. A real life data on TRIP steel is also analysed using the proposed method. [Copyright &y& Elsevier]

Abstract: Managers in call centers use metrics to measure organizational performance; unfortunately, these metrics only reveal how well service agents process calls. To achieve substantial quality improvement, managers need to obtain in-depth information from operational metrics such as first call resolution (FCR). This research incorporated rough set theory (RST) in analyzing FCR to produce decision rules. RST has been known to reduce the number of attributes and attribute values without affecting the original results. Data from a tariff (industry segment) call center in Taiwan were used in this study, and a four-step process was used to produce the final decision rules. The results were verified by a 10-fold cross-validation process against the decision rules produced without applying RST. The decision rules produced with RST are as effective as those produced without, but with reduced number of attributes and attribute values. This reduced decision rule set can help managers analyze the current operator procedures more efficiently and subsequently improve the call center efficiency. Without affecting effectiveness, the RST application reduces the needed number of attributes and attribute values to produce a more compact decision rule set, and this increased efficiency, in turn, allows managers to delve deeper into the operational factors. [Copyright &y& Elsevier]

Abstract: Rough set approach is one of effective feature selection methods that can preserve the meaning of the features. So far, many feature selection (also called feature reduction) methods based on Rough set have been proposed. Of which, methods based on discernibility matrix are of considerable benefits for their conciseness and effectiveness, but have much higher space complexity. In order to reduce the storage space of the existing feature selection methods based on discernibility matrix, a novel condensing tree ( C-Tree ) structure was introduced, which is an extended order-tree, every nonempty element of a discernibility matrix is stored in one path in the C-Tree by given order of features and lots of nonempty elements share one path or sub-path, so the C-Tree has much lower space complexity as compared to discernibility matrix. However, the size of a C-Tree greatly depends on the order of features in most cases, hence how to set the proper order of features is of importance. To generate a higher compressed C-Tree , in this paper, after introducing an efficient trick for efficiently measuring the relative importance of every feature, we present a new feature ordering strategy according to the descending order of their importance. Further, based on the new feature ordering strategy, corresponding two heuristic algorithms for feature selection are introduced. Algorithms of this paper are experimented using six standard datasets and five synthetic datasets for testing both time and space complexities. Experimental results show that the newly improved feature selection algorithm can further efficiently reduce cost of storage in most cases. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, concepts of knowledge granulation, knowledge entropy and knowledge uncertainty measure are given in ordered information systems, and some important properties of them are investigated. From these properties, it can be shown that these measures provides important approaches to measuring the discernibility ability of different knowledge in ordered information systems. And relationship between knowledge granulation, knowledge entropy and knowledge uncertainty measure are considered. As an application of knowledge granulation, we introduce definition of rough entropy of rough sets in ordered information systems. By an example, it is shown that the rough entropy of rough sets is more accurate than classical rough degree to measure the roughness of rough sets in ordered information systems. [Copyright &y& Elsevier]

Abstract: A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification. [Copyright &y& Elsevier]

Feature selection is a vital dimensionality reduction technology for machine learning and data mining that aims to select a minimal subset from the original feature space. Traditional feature selection methods assume that all features can be required before learning, while features may exist in a stream mode for some real-world applications. Therefore, online streaming feature selection was proposed to handle streaming features on the fly. When the feature dimension is extraordinarily high or even infinite, it is time-consuming or impractical to wait for all the streaming features to arrive. Motivated by this, we study and solve the exciting issue of whether we can terminate the online streaming feature selection early for efficiency while maintaining satisfactory performance for the first time. Specifically, we first formally define the problem of online early terminated streaming feature selection and summary two properties that the early terminated mapping function should satisfy. Then we choose the dependency degree function in Rough Set theory as our early terminated mapping function and demonstrate that it satisfies the two properties. Based on this, we propose a novel Early Terminated Online Streaming Feature Selection framework, named OSFS-ET, which could terminate the streaming feature selection early before the end of streaming features and guarantee a competing performance with the currently selected features. Extensive experiments on twelve real-world datasets demonstrate that OSFS-ET can be far faster than state-of-the-art streaming feature selection methods while maintaining excellent performance on predictive accuracy. • First present the issue of how to terminate online streaming feature selection early. • Propose a novel early terminated online streaming feature selection framework. • Extensive experiments demonstrate the effectiveness of our new framework. [ABSTRACT FROM AUTHOR]