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Chinese Journal of Electronics 2019年第4期28卷 725-736页

作者： WANG Guangqiong DaZhou Industrial Technology Institute of Intelligent Manufacturing School of Intelligent Manufacturing Sichuan University of Arts and Science

Attribute reduction, also known as feature selection, is a vital application of rough set theory in areas such as machine learning and data mining. With several information systems constantly and dynamically changing in reality, the method of continuing the incremental attribute reduction for these dynamic information systems is the focus of this research. In an incomplete information system, the increasing form of attribute sets is an important form of dynamic *** this paper, the definition of conditional entropy is first introduced in the incomplete information system,and for the circumstances of the dynamic change of the attribute sets, two types of incremental mechanisms of the matrix and non-matrix forms based on conditional entropy are subsequently proposed. In addition, on the basis of the two incremental mechanisms, the incremental attribute reduction algorithm is given when the attribute set increases dynamically. Finally, the experimental results of the UCI(University of California Irvine) datasets verify that the two proposed incremental algorithms exhibit a superior performance with regard to attribute reduction when compared with the non-incremental attribute reduction algorithm, which in turn is superior to other relative incremental algorithms.

关键词： Attribute reduction Incremental learning incomplete information system Rough set Conditional entropy

A rule induction algorithm for incomplete information system

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Journal of Shanghai University(English Edition) 2006年第6期10卷 506-509页

作者：瞿彬彬卢炎生肖兵 College of Computer Science and Technology Huazhong University of Science and Technology

incompleteness of information about objects may be the greatest obstruct to performing induction learning from examples. In this paper, the concept of limited-non-symmetric similarity relation is used to formulate a new definition of approximation to an incomplete information system. With the new definition of approximation to an object set and the concept of attribute value pair, rough-setsbased methodology for certain rule acquisition in an incomplete information system is developed. The algorithm can deal with incomplete data directly and does not require changing the size of the original incomplete system. Experiments show that the algorithm provides precise and simple certain decision rules and is not affected by the missing values.

关键词： rough sets incomplete information system limited-non-symmetric similarity relation rule induction.

Attribute reduction of incomplete information system using characteristic multigranulation model

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Attribute reduction of incomplete information system using c...

Generalized binary discernibility matrix for attribute reduction in incomplete information systems

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作者： Jun Yu Bo Ding Liwei Deng School of Automation Harbin University of Science and Technology School of Computer Science and Technology Harbin University of Science and Technology

In order to extract reliable decision rules from an incomplete information system(IIS) that simultaneously contains two kinds of unknown attribute values, an attribute reduction algorithm using characteristic multigranulation model is put forward in this paper. First, characteristic sets that satisfy characteristic relation are determined according to the IIS and the attribute subset. Calculate the attribute dependency of the decision classes with respect to the attribute subset. Then, determine whether all condition attribute values are indispensable respectively in terms of the attribute dependencies. Finally, delete all the redundant condition attribute values, and generate decision rules. So as to validate the effectiveness of the proposed algorithm, two numerical experiments were carried out. The experimental analysis indicates the proposed algorithm is more efficient and accurate.

关键词： Attribute reduction incomplete information system Characteristic relation Multigranulation model

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The Journal of China Universities of Posts and Telecommunications 2017年第4期24卷 57-68,75页

作者： Ma Fumin Zhang Tengfei College of information Engineering Nanjing University of Finance and Economics College of Automation Nanjing University of Posts and Telecommunications

To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.

关键词： rough set generalized binary discernibility matrix attribute relative reduction incomplete information system

Mining association rules in incomplete information systems

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Journal of Central South University of Technology 2008年第5期15卷 733-737页

作者：罗可王丽丽童小娇 School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410076 China School of Computer and Communication Engineering Changsha University of Science and Technology Department of Computer Science and Technology Dezhou University Dezhou 253023 China

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

关键词： association rules rough sets prediction support prediction confidence incomplete information system

Extension of information entropy-based measures in incomplete information systems

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Journal of Harbin Institute of Technology(New Series) 2005年第5期12卷 544-550页

作者：李仁璞黄道高茂庭 College of Computer Science and Technology Yantai Normal University Yantai 264025 China College of information East China University of Science and Technology Shanghai 200237 China Dept. of Computer Science and Engineering Shanghai Maritime University Shanghai 200135 China

It is helpful for people to understand the essence of rough set theory to study the concepts and operations of rough set theory from its information view. In this paper we address knowledge expression and knowledge reduction in incomplete infolvnation systems from the information view of rough set theory. First, by extending information entropy-based measures in complete information systems, two new measures of incomplete entropy and incomplete conditional entropy are presented for incomplete information systems. And then, based on these measures the problem of knowledge reduction in incomplete information systems is analyzed and the reduct definitions in incomplete information system and incomplete decision table are proposed respectively. Finally, the reduct definitions based on incomplete entropy and the reduct definitions based on similarity relation are compared. Two equivalent relationships between them are proved by theorems and an in equivalent relationship between them is illustrated by an example. The work of this paper extends the research of rough set theory from information view to incomplete information systems and establishes the theoretical basis for seeking efficient algorithm of knowledge acquisition in incomplete information systems.

关键词： rough set theory information entropy incomplete information system knowledge reduction

Rough set extensions in incomplete information systems

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Frontiers of Electrical and Electronic Engineering in China 2008年第4期3卷 399-405页

作者： Guoyin WANG Lihe GUAN Feng HU School of information Science and Technology Southwest Jiaotong UniversityChengdu 610031China Institute of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of information and Calculation Science Chongqing Jiaotong UniversityChongqing 400074Chin

All eight possible extended rough set models in incomplete information systems are *** analyzing existing extended models and technical meth-ods of rough set theory,the strategy of model extension is found to be suitable for processing incomplete information systems instead of filling possible values for missing *** analyzing the definitions of existing extended models,a new general extended model is *** new model is a generalization of indiscernibility relations,tolerance relations and non-symmetric similarity ***,suggestions for further study of rough set theory in incomplete informa-tion systems are put forward.

关键词： rough set incomplete information system extended model

Tolerance-based multigranulation rough sets in incomplete systems

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Frontiers of Computer Science 2014年第5期8卷 753-762页

作者： Zaiyue ZHANG Xibei YANG School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Perception and systems for High-Dimensional information (Nanjing University of Science and Technology) Ministry of Education Nanjing 210094 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong 643000 China

Presently, the notion of multigranulation has been brought to our attention. In this paper, the multigranulation technique is introduced into incomplete information systems. Both tolerance relations and maximal consistent blocks are used to construct multigranulation rough sets. Not only are the basic properties about these models studied, but also the relationships between different multigranulation rough sets are explored. It is shown that by using maximal consistent blocks, the greater lower approximation and the same upper approximation as from tolerance relations can be obtained. Such a result is consistent with that of a single-granulation framework.

关键词： incomplete information system maximal con-sistent block multigranulation rough sets tolerance relation

A Granular Computing Model Based on Tolerance relation

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The Journal of China Universities of Posts and Telecommunications 2005年第3期12卷 86-90页

作者： WANG Guo-yin, HU Feng, HUANG Hai, WU YuChongqing University of Posts and Telecommunications, Chongqing 400065, P.R. China Chongqing University of Posts and Telecommunications Chongqing 400065

Granular computing is a new intelligent computing theory based on partition of problem concepts. It is an important problem in Rough Set theory to process incomplete information systems directly. In this paper, a granular computing model based on tolerance relation for processing incomplete information systems is developed. Furthermore, a criteria condition for attribution necessity is proposed in this model.

关键词： incomplete information system granular computing rough set tolerance relation

Extended Rough Set Model Based on Prior Probability and Valued Tolerance Relation

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Journal of Electronic Science and Technology 2011年第1期9卷 46-50页

作者： Hao-Dong Zhu Hong-Chan Li the School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450002 China

Based on equivalence relation,the classical rough set theory is unable to deal with incomplete information *** this case,an extended rough set model based on valued tolerance relation and prior probability obtained from incomplete information systems is firstly *** a part of the model,the corresponding discernibility matrix and an attribute reduction of incomplete information system are then ***,the extended rough set model and the proposed attribute reduction algorithm are verified under an incomplete information system.

关键词： Attribute reduction discernibility matrix incomplete information system prior probability,rough set.