Showing papers on "Rough set published in 2018"

Modified cuckoo search algorithm with rough sets for feature selection

Abstract: In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

Maximal-Discernibility-Pair-Based Approach to Attribute Reduction in Fuzzy Rough Sets

Abstract: Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature selection as the rough set theory is, it can only handle symbolic attributes. In order to overcome this drawback, the fuzzy rough set model is proposed, which is an extended model of rough sets and is able to deal with imprecision and uncertainty in both symbolic and numerical attributes. The existing attribute selection algorithms based on the fuzzy rough set model mainly take the angle of “attribute set,” which means they define the object function representing the predictive ability for an attribute subset with regard to the domain of discourse, rather than following the view of an “object pair.” Algorithms from the viewpoint of the object pair can ignore the object pairs that are already discerned by the selected attribute subsets and, thus, need only to deal with part of object pairs instead of the whole object pairs from the discourse, which makes such algorithms more efficient in attribute selection. In this paper, we propose the concept of reduced maximal discernibility pairs, which directly adopts the perspective of the object pair in the framework of the fuzzy rough set model. Then, we develop two attribute selection algorithms, named as reduced maximal discernibility pairs selection and weighted reduced maximal discernibility pair selection, based on the reduced maximal discernibility pairs. Experiment results show that the proposed algorithms are effective and efficient in attribute selection.

A group incremental feature selection for classification using rough set theory based genetic algorithm

Abstract: Display Omitted In proposed method, incremental learning technique has been investigated.A novel incremental feature selection algorithm is proposed for classification analysis.Incremental feature selection method is devised based on rough set theory and genetic algorithm.Experimental results show the effectiveness in terms of accuracy and time complexity. Data Mining is one of the most challenging tasks in a dynamic environment due to rapid growth of data with respect to time. Dimension reduction, the key process of relevant feature selection, is applied prior to extracting interesting patterns or information from large repositories of data. In a dynamic environment, newly generated group of data together with the information extracted from the previous data are analyzed to select the most relevant and important features of the entire data set. As a result, efficiency and acceptability of the incremental feature selection model increase in the field of data mining. In our paper, a group incremental feature selection algorithm is proposed using rough set theory based genetic algorithm for selecting the optimized and relevant feature subset, called reduct. The objective function of the genetic algorithm used for incremental feature selection is defined using the previously generated reduct and positive region of the target set, concepts of rough set theory. The method may be applied in a regular basis in the dynamic environment after small to moderate volume of data being added into the system and thus the computational time, the major issue of the genetic algorithm does not affect the proposed method. Experimental results on benchmark datasets demonstrate that the proposed method provides satisfactory results in terms of number of selected features, computation time and classification accuracies of various classifiers.

A survey of decision making methods based on two classes of hybrid soft set models

Abstract: To the best of our knowledge, the tool of soft set theory is a new efficacious technique to dispose uncertainties and it focuses on the parameterization, while fuzzy set theory emphasizes the truth degree and rough set theory as another tool to handle uncertainties, it places emphasis on granular. However, the real-world problems that under considerations are usual very complicated. Consequently, it is very difficult to solve them by a single mathematical tool. It is worth noting that decision making (briefly, DM) in an imprecise environment has been showing more and more role in real-world applications. Researches on the idiographic applications of the above three uncertain theories as well as their hybrid models in DM have attracted many researchers’ widespread interest. DM methods are not yet proposed based on fusions of the above three uncertain theories. In view of the reason, by compromising the above three uncertain theories, we elaborate some reviews to DM methods based on two classes of hybrid soft models: SRF-sets and SFR-sets. We test all algorithms for DM and computation time on data sets produced by soft sets and FS-sets. The numerical experimentation programs are written for given pseudo codes in MATLAB. At the same time, the comparisons of all algorithms are given. Finally, we expatiate on an overview of techniques based on the involved hybrid soft set models.

Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets

Abstract: In complex pattern recognition tasks, objects are typically characterized by means of multimodality attributes, including categorical, numerical, text, image, audio, and even videos. In these cases, data are usually high dimensional, structurally complex, and granular. Those attributes exhibit some redundancy and irrelevant information. The evaluation, selection, and combination of multimodality attributes pose great challenges to traditional classification algorithms. Multikernel learning handles multimodality attributes by using different kernels to extract information coming from different attributes. However, it cannot consider the aspects fuzziness in fuzzy classification. Fuzzy rough sets emerge as a powerful vehicle to handle fuzzy and uncertain attribute reduction. In this paper, we design a framework of multimodality attribute reduction based on multikernel fuzzy rough sets. First, a combination of kernels based on set theory is defined to extract fuzzy similarity for fuzzy classification with multimodality attributes. Then, a model of multikernel fuzzy rough sets is constructed. Finally, we design an efficient attribute reduction algorithm for large scale multimodality fuzzy classification based on the proposed model. Experimental results demonstrate the effectiveness of the proposed model and the corresponding algorithm.

Local neighborhood rough set

Abstract: With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes.

Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Abstract: Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole dataset in batch, which is often costly or even intractable for large datasets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance.

A Novel TODIM Method-Based Three-Way Decision Model for Medical Treatment Selection

Abstract: Three-way decision models and their relative applications have received a great deal of research attention. Most of these models were constructed on the basis of decision-theoretic rough sets (DTRSs) and Bayesian decision theory, both of which ignore the risk preferences of decision-makers. To address this shortcoming, this paper constructs a novel TODIM method-based three-way decision model and demonstrates its use in the context of online diagnosis and medical treatment selection. This model combines information systems and DTRSs together to construct a hybrid information system. And it solves the problems in aggregating cost-loss information with different levels of importance by utilizing the power average operator. Furthermore, an extension of the TODIM method is proposed based on the novel possibility degree measurement considering the probability distribution of loss functions. To validate the reasonableness and effectiveness of our model, we give a series of simulation experiments related to treatment selection for a Good Doctor Online user infected with the common cold.

Rough Concept Analysis

Abstract: The theory introduced, presented and developed in this paper, is concerned with Rough Concept Analysis. This theory is a synthesis of the theory of Rough Sets pioneered by Zdzislaw Pawlak with the theory of Formal Concept Analysis pioneered by Rudolf Wille. The central notion in this paper of a rough formal concept combines in a natural fashion the notion of a rough set with the notion of a formal concept: "rough set + formal concept = rough formal concept". A follow-up paper will provide a synthesis of the two important data modeling techniques: conceptual scaling of Formal Concept Analysis and Entity-Relationship database modeling.

A Novel Decision-Making Method Based on Rough Fuzzy Information

Abstract: Fuzzy sets and rough sets are two different mathematical models to cope with vagueness, but they are correlated. Dubois and Prade combined these two sets to make new hybrid models including fuzzy rough sets and rough fuzzy sets. In this research study, we introduce several basic notions, concerning rough fuzzy digraph, and investigate some related properties. We present applications of rough fuzzy digraphs in decision-making problems. In particular, we develop efficient algorithms to solve decision-making problems.

Hesitant fuzzy linguistic rough set over two universes model and its applications

Abstract: In practical decision making situations, decision makers usually express preferences by evaluating qualitative linguistic alternatives using the hesitant fuzzy linguistic term set. To analyze the hesitant fuzzy linguistic information effectively, we aim to apply the rough set over two universes model. Thus, it is necessary to study the fusion of the hesitant fuzzy linguistic term set and rough set over two universes. This paper proposes a general framework for the study of the hesitant fuzzy linguistic rough set over two universes. First, both the definitions and some fundamental properties will be developed, followed by construction of a general decision making rule based on the hesitant fuzzy linguistic information. Finally, we illustrate the newly proposed approach according to the basis of person-job fit, and discuss its applications compared to classical methods.

A Novel Three-way decision model with decision-theoretic rough sets using utility theory

Abstract: In the classical three-way decision (3WD) model with decision-theoretic rough sets (DTRSs), the classification correct rate (CCR) is an important issue. As one of the risk measurement methods, loss functions have been used to calculate thresholds. Using risk measurement methods relevant research has yielded many results. However, for improving the CCR, few research studies have focused on the risk measurement by considering the difference among the equivalence classes. In this paper, from the viewpoint of the difference among the equivalence classes, to improve the CCR, a novel model is proposed to derive the 3WD model with DTRSs by considering the new risk measurement functions through the utility theory. First, the weight of each attribute is calculated based on the knowledge distance. Then, with the aid of utility theory, the improved utility function, which can score the attribute values, is defined. Further, a reasonable model for constructing the utility-based scoring functions is proposed. Then, a decision procedure for calculating the exclusive thresholds is designed and the rules of three-way decisions (3WDs) are deduced. An example is presented to illustrate the proposed model and the trend of change for exclusive thresholds. Finally, our experimental results show that the performance of the proposed model is better than that of current existing models.

Rough sets: past, present, and future.

Abstract: Introduction of rough sets by Professor Zdzislaw Pawlak has completed 35 years. The theory has already attracted the attention of many researchers and practitioners, who have contributed essentially to its development, from all over the world. The methods, developed based on rough set theory alone or in combination with other approaches, found applications in many areas. In this article, we outline some selected past and present research directions of rough sets. In particular, we emphasize the importance of searching strategies for relevant approximation spaces as the basic tools in achieving computational building blocks (granules or patterns) required for approximation of complex vague concepts. We also discuss new challenges related to problem solving by intelligent systems (IS) or complex adaptive systems (CAS). The concern is to control problems using interactive granular computing, an extension of the rough set approach, for effective realization of computations realized in IS or CAS. These challenges are important for the development of natural computing too.

Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures

Abstract: The outlier relies on its distinctive mechanism and valuable information to play an important role in expert and intelligent systems, and thus outlier detection has already been extensively applied in relevant fields including the fraud detection, medical diagnosis, public security, etc. The outlier detection methods of rough sets recently gain in-depth research, because they are data-driven and never require additional knowledge. However, classical rough set-based methods consider only categorical data; furthermore, neighborhood rough sets adhere to numeric and heterogeneous data, but their outlier detection is mainly restricted to numeric data now. According to the hybrid data-driving, this paper investigates outlier detection by the neighborhood information entropy and its developmental measures, and the applicable data sets widely concern categorical, numeric, and mixed data; as a result, the new method extends both the traditional distance-based and rough set-based methods to enrich outlier detection. Concretely, the neighborhood information system is first determined by the heterogeneous distance and self-adapting radius, the neighborhood information entropy is then defined to implement whole uncertainty measurement, three gradual information measures are further constructed to describe each single object, and finally the neighborhood entropy-based outlier factor (NEOF) is integratedly established to detect outliers; moreover, the NEOF-based outlier detection algorithm (called the NIEOD algorithm) is designed and applied. By virtue of UCI data experiments, the NIEOD algorithm is compared with six existing detection algorithms (including the NED, IE, SEQ, FindCBLOF, DIS, KNN algorithms), and the concrete results generally reflect the better effectiveness and adaptability of the new method.

Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction

Abstract: Due to technological advancement and the explosive growth of electrically stored information, automated methods are required to aid users in maintaining and processing this huge amount of information. Experts, as well as machine learning processes on large volumes of data, are the main sources of knowledge. Knowledge extraction is an important step in framing expert and intelligent systems. However, the knowledge extraction phase is very slow or even impossible due to noise and large size of data. To enhance the productivity of machine learning algorithms, feature selection or attribute reduction plays a key role in the selection of relevant and non-redundant features to improve the performance of classifiers and interpretability of data. Many areas like machine learning, image processing, data mining, natural language processing and Bioinformatics, etc., which have high relevancy to expert and intelligent systems, are applications of feature selection. Rough set theory has been successfully applied for attribute reduction, but this theory is inadequate in the case of attribute reduction of real-valued data set as it may lose some information during the discretization process. Fuzzy and rough set theories have been combined and various attribute selection techniques were proposed, which can easily handle the real-valued data. An intuitionistic fuzzy set possesses a strong ability to represent information and better describing the uncertainty when compared to the classical fuzzy set theory as it considers positive, negative and hesitancy degree simultaneously for an object to belong to a set. This paper proposes a novel mechanism of attribute selection using tolerance-based intuitionistic fuzzy rough set theory. For this, we present tolerance-based intuitionistic fuzzy lower and upper approximations and formulate a degree of dependency of decision features over the set of conditional features. Moreover, the basic results on lower and upper approximations based on rough sets are extended for intuitionistic fuzzy rough sets and analogous results are established. In the end, the proposed algorithm is applied to an example data set and the comparison between tolerance-based fuzzy rough and intuitionistic fuzzy rough sets approaches for feature selection is presented. The proposed concept is found to be better performing in the form of selected attributes.

VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images

Abstract: Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNNs), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterize the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery and partition this uncertainty into positive regions (correct classifications) and nonpositive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a multilayer perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as semantic labeling data sets. The MRF-CNN consistently outperformed the benchmark MLP, support vector machine, MLP-MRF, CNN, and the baseline methods. This paper provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification.

Bipolar Neutrosophic Soft Sets and Applications in Decision Making

Abstract: Neutrosophic set, proposed by Smarandache considers a truth membership function, an indeterminacy membership function and a falsity membership function. Soft set, proposed by Molodtsov is a mathematical framework which has the ability of independency of parameterizations inadequacy, syndrome of fuzzy set, rough set, probability.

Attribute reduction based on max-decision neighborhood rough set model

Abstract: The neighborhood rough set model only focuses on the consistent samples whose neighborhoods are completely contained in some decision classes, and ignores the divisibility of the boundary samples whose neighborhoods can not be contained in any decision classes. In this paper, we pay close attention to the boundary samples, and enlarge the positive region by adding the samples whose neighborhoods have maximal intersection with some decision classes. Applying the mentioned idea, we introduce a new neighborhood rough set model, named max-decision neighborhood rough set model. An attribute reduction algorithm is designed based on the model. Both theoretical analysis and experimental results show that the proposed algorithm is effective for removing most redundant attributes without loss of classification accuracy.

Feature selection based on maximal neighborhood discernibility

Abstract: Neighborhood rough set has been proven to be an effective tool for feature selection. In this model, the positive region of decision is used to evaluate the classification ability of a subset of candidate features. It is computed by just considering consistent samples. However, the classification ability is not only related to consistent samples, but also to the ability to discriminate samples with different decisions. Hence, the dependency function, constructed by the positive region, cannot reflect the actual classification ability of a feature subset. In this paper, we propose a new feature evaluation function for feature selection by using discernibility matrix. We first introduce the concept of neighborhood discernibility matrix to characterize the classification ability of a feature subset. We then present the relationship between distance matrix and discernibility matrix, and construct a feature evaluation function based on discernibility matrix. It is used to measure the significance of a candidate feature. The proposed model not only maintains the maximal dependency function, but also can select features with the greatest discernibility ability. The experimental results show that the proposed method can be used to deal with heterogeneous data sets. It is able to find effective feature subsets in comparison with some existing algorithms.