Changzhong Wang

A novel method for attribute reduction of covering decision systems

Attribute reduction has become an important step in pattern recognition and machine learning tasks. Covering rough sets, as a generalization of classical rough sets, have attracted wide attention in both theory and application. This paper provides a novel method for attribute reduction based on covering rough sets. We review the concepts of consistent and inconsistent covering decision systems and their reducts and we develop a judgment theorem and a discernibility matrix for each type of covering decision system. Furthermore, we present some basic structural properties of attribute reduction with covering rough sets. Based on a discernibility matrix, we develop a heuristic algorithm to find a subset of attributes that approximate a minimal reduct. Finally, the experimental results for UCI data sets show that the proposed reduction approach is an effective technique for addressing numerical and categorical data and is more efficient than the method presented in the paper [D.G. Chen, C.Z. Wang, Q.H. Hu, A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets, Information Sciences 177(17) (2007) 3500-3518].

Feature subset selection based on fuzzy neighborhood rough sets

Rough set theory has been extensively discussed in machine learning and pattern recognition. It provides us another important theoretical tool for feature selection. In this paper, we construct a novel rough set model for feature subset selection. First, we define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. A parameterized fuzzy relation is introduced to characterize fuzzy information granules for analysis of real-valued data. Then, we use the relationship between fuzzy neighborhood and fuzzy decision to construct a new rough set model: fuzzy neighborhood rough set model. Based on this model, the definitions of upper and lower approximation, boundary region and positive region are given, and the effects of parameters on these concepts are discussed. To make the new model tolerate noises in data, we introduce a variable-precision fuzzy neighborhood rough set model. This model can decrease the possibility that a sample is classified into a wrong category. Finally, we define the dependency between fuzzy decision and condition attributes and employ the dependency to evaluate the significance of a candidate feature, using which a greedy feature subset selection algorithm is designed. The proposed algorithm is compared with some classical algorithms. The experiments show that the proposed algorithm gets higher classification performance and the numbers of selected features are relatively small.

Data compression with homomorphism in covering information systems

In reality we are always faced with a large number of complex massive databases. In this work we introduce the notion of a homomorphism as a kind of tool to study data compression in covering information systems. The concepts of consistent functions related to covers are first defined. Then, by classical extension principle the concepts of covering mapping and inverse covering mapping are introduced and their properties are studied. Finally, the notions of homomorphisms of information systems based on covers are proposed, and it is proved that a complex massive covering information system can be compressed into a relatively small-scale information system and its attribute reduction is invariant under the condition of homomorphism, that is, attribute reductions in the original system and image system are equivalent to each other.

An improved attribute reduction scheme with covering based rough sets

A simpler approach to attribute reduction based on discernibility matrix is presented with covering based rough sets.Some important properties of attribute reduction with covering based rough sets are improved.The computational complexity of the improved reduction approach is relatively reduced.A new algorithm to attribute reduction in decision tables is presented in a different strategy of identifying objects. Attribute reduction is viewed as an important preprocessing step for pattern recognition and data mining. Most of researches are focused on attribute reduction by using rough sets. Recently, Tsang et al. discussed attribute reduction with covering rough sets in the paper (Tsang et al., 2008), where an approach based on discernibility matrix was presented to compute all attribute reducts. In this paper, we provide a new method for constructing simpler discernibility matrix with covering based rough sets, and improve some characterizations of attribute reduction provided by Tsang et al. It is proved that the improved discernibility matrix is equivalent to the old one, but the computational complexity of discernibility matrix is relatively reduced. Then we further study attribute reduction in decision tables based on a different strategy of identifying objects. Finally, the proposed reduction method is compared with some existing feature selection methods by numerical experiments and the experimental results show that the proposed reduction method is efficient and effective.

A Fitting Model for Feature Selection With Fuzzy Rough Sets

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy rough dependency as a criterion for feature selection. However, this model can merely maintain a maximal dependency function. It does not fit a given dataset well and cannot ideally describe the differences in sample classification. Therefore, in this study, we introduce a new model for handling this problem. First, we define the fuzzy decision of a sample using the concept of fuzzy neighborhood. Then, a parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced. This can guarantee that the membership degree of a sample to its own category reaches the maximal value. Furthermore, this approach can fit a given dataset and effectively prevents samples from being misclassified. Finally, we define the significance measure of a candidate attribute and design a greedy forward algorithm for feature selection. Twelve datasets selected from public data sources are used to compare the proposed algorithm with certain existing algorithms, and the experimental results show that the proposed reduction algorithm is more effective than classical fuzzy rough sets, especially for those datasets for which different categories exhibit a large degree of overlap.

Communication between information systems with covering based rough sets

Communication between information systems is considered as an important issue in granular computing. The concept of homomorphism is an effective mathematical tool to study information exchange between information systems. This paper provides a study on some basic properties of covering information systems and decision systems under homomorphisms. First, we define consistent functions related to coverings and covering mappings between two universes, and study their properties. Then, we introduce the notions of homomorphisms of covering information systems and point out that a homomorphism is a special covering mapping between information systems. Furthermore, we investigate some important properties of homomorphisms in covering information systems and decision systems. It is proved that some basic properties of original systems, such as set approximations, attribute reductions, can be reserved under the condition of homomorphisms in both covering information systems and covering decision systems.

Feature Selection Based on Neighborhood Discrimination Index

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be used to distinguish samples with different decisions. In this paper, a neighborhood discrimination index is proposed to characterize the distinguishing information of a neighborhood relation. It reflects the distinguishing ability of a feature subset. The proposed discrimination index is computed by considering the cardinality of a neighborhood relation rather than neighborhood similarity classes. Variants of the discrimination index, including joint discrimination index, conditional discrimination index, and mutual discrimination index, are introduced to compute the change of distinguishing information caused by the combination of multiple feature subsets. They have the similar properties as Shannon entropy and its variants. A parameter, named neighborhood radius, is introduced in these discrimination measures to address the analysis of real-valued data. Based on the proposed discrimination measures, the significance measure of a candidate feature is defined and a greedy forward algorithm for feature selection is designed. Data sets selected from public data sources are used to compare the proposed algorithm with existing algorithms. The experimental results confirm that the discrimination index-based algorithm yields superior performance compared to other classical algorithms.

Communication Between Information Systems Using Fuzzy Rough Sets

Communication between information systems is a basic problem in granular computing, and the concept of homomorphism is a useful mathematical tool to study this problem. In this paper, some properties of communication between information systems based on fuzzy rough sets are investigated. The concepts of fuzzy relation mappings between universes are first proposed in order to construct a fuzzy relation of one universe according to the given fuzzy relation on the other universe. The main properties of the mappings are studied. The notions of homomorphism of information systems based on fuzzy rough sets are then proposed, and it is proved that properties of relation operations in the original information system and structural features of the system, such as approximations of arbitrary fuzzy sets and attribute reductions, are guaranteed in its image system under the condition of homomorphism.

A unified information measure for general binary relations

Abstract Shannons entropy and its variants have been applied to measure uncertainty in a variety of special binary relations. However, few studies have been conducted on uncertainty of general binary relations. In this study, we present a unified form of uncertainty measures for general binary relations. We redefine the concepts of entropy, joint entropy, conditional entropy, and mutual information. These uncertainty measures are generalizations of corresponding measures of special relations. We study the relationship between these measures and examine important properties. Finally, numerical experiments are performed to identify applications of the proposed uncertainty measures. Comparing with existing uncertainty measures, the proposed method not only addresses the uncertainty of heterogeneous data sets, but also exhibit better performance in attribute reduction. This study can provide a fundamental framework for uncertainty theories of special rough set models.

Fuzzy information systems and their homomorphisms

Abstract With the arrival of the information age, information acquisition and communication have become more and more important in the field of information technology. This paper uses the concept of homomorphism as a basic tool to study the communication between fuzzy information systems. The concepts of consistent and compatible mappings with respect to fuzzy sets are firstly defined and their basic properties are studied. Then, a pair of lower and upper rough fuzzy approximation operators is constructed by means of the concept of fuzzy mappings. Basic invariant properties of the approximation operators are investigated. Finally, the concepts of fuzzy information system and its homomorphism are introduced, and some invariant properties of fuzzy information systems under homomorphisms are examined. It is proved that the attribute reductions of an original information system and its image system are equivalent to each other in the context of fuzzy attributes. These results may have potential applications in attribute reduction and classification issues.