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attribute reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma JunWang Wenchang Yu Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

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Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its attribute reduction

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Intelligent Automation & Soft Computing 2023年第8期37卷 2059-2075页

作者： Changshun Liu Yan Liu Jingjing Song Taihua Xu School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhejiang Ocean UniversityZhoushan316022China

attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

关键词： attribute reduction information granulation mapping-equiva-lence relation neighborhood rough set

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attribute reduction theory of concept lattice based on decision formal contexts

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Science in China(Series F) 2008年第7期51卷 910-923页

作者： WEI Ling QI JianJun ZHANG WenXiu Department of Mathematics Northwest University Xi'an 710069 China School of Computer Science & Technology Xidian University Xi'an 710071 China Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049China

The theory of concept lattices is an efficient tool for knowledge representation and knowledge discovery, and is applied to many fields successfully. One focus of knowledge discovery is knowledge reduction. Based on the reduction theory of classical formal context, this paper proposes the definition of decision formal context and its reduction theory, which extends the reduction theory of concept lattices. In this paper, strong consistence and weak consistence of decision formal context are defined respectively. For strongly consistent decision formal context, the judgment theorems of consistent sets are examined, and approaches to reduc- tion are given. For weakly consistent decision formal context, implication mapping is defined, and its reduction is studied. Finally, the relation between reducts of weakly consistent decision formal context and reducts of implication mapping is discussed.

关键词： concept lattice decision formal context attribute reduction discernibility matrix implication mapping

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Valid Incremental attribute reduction Algorithm Based on attribute Generalization for an Incomplete Information System

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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 change.In 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

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attribute reduction on measuring data in neighborhood rough set with common-test-cost and error ranges

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科技展望 2017年第18期27卷 257-261+266页

作者：刘忠慧西南石油大学计算机科学学院

Many previous studies on measuring data attempted to seek an optimal reduct to achieve a low total test cost,which is based on an assumption that all attributes are independent. In many real-world measurements,however,this assumption is not reasonable due to the affection of some attributes being related. To address this issue,we firstly define decision systems with test-costs,common-test-costs and error ranges for data reduction. The concepts of reduction which include lower and upper approximations,positive regions,and relative reducts are based on neighborhood rough sets. Then we design a heuristic algorithms to attack measuring data sets reduction. In the process of reduction,common-test-costs are used to improve prune strategy and develop information function for algorithm ’s efficiency. The experiments were conducted on 7 UCI measuring database,which validated the effectiveness of proposed algorithms.

关键词： attribute reduction Neighborhood rough set Error ranges Common-test-costs Heuristic algorithm

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attribute reduction theory and approach to concept lattice

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Science in China(Series F) 2005年第6期48卷 713-726页

作者： ZHANG Wenxiu WEI Ling QI Jianjun Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Department of Mathematics Northwest University Xi'an 710069 China Institute of Computer Architecture & Network Xi'an Jiaotong University Xi'an 710049 China

The theory of the concept lattice is an efficient tool for knowledge representation and knowledge discovery, and is applied to many fields successfully. One focus of knowledge discovery is knowledge reduction. This paper proposes the theory of attribute reduction in the concept lattice, which extends the theory of the concept lattice. In this paper, the judgment theorems of consistent sets are examined, and the discernibility matrix of a formal context is introduced, by which we present an approach to attribute reduction in the concept lattice. The characteristics of three types of attributes are analyzed.

关键词： formal context concept lattice attribute reduction discernibility matrix

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Fault attribute reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

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Journal of Harbin Institute of Technology(New Series) 2020年第6期27卷 83-90页

作者： Li Bian Hui He Hongna Sun Wenjing Liu College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang 524088GuangdongChina College of Electrical and Control Engineering Heilongjiang University of Science and TechnologyHarbin 150022China Handan Power Supply Company Handan 056002HebeiChina

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.

关键词： transformer fault improved imperialist competitive algorithm rough set attribute reduction BP neural network

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attribute reduction of incomplete information system using characteristic multigranulation model

Attribute reduction of incomplete information system using c...

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第30届中国控制与决策会议

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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attribute reduction Method Based on Sequential Three-Branch Decision Model

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Applied Mathematics 2024年第4期15卷 257-266页

作者： Peiyu Su Fu Li The Faculty of Mathematics and Statistics Qinghai Nationalities University Xining Qinghai

attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.

关键词： attribute reduction Three-Branch Decision Sequential Three-Branch Decision

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Fusing Supervised and Unsupervised Measures for attribute reduction

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Intelligent Automation & Soft Computing 2023年第7期37卷 561-581页

作者： Tianshun Xing Jianjun Chen Taihua Xu Yan Fan School of Computer Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China

It is well-known that attribute reduction is a crucial action of rough set.The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations.Normally,the learning performance of attributes in derived reduct is much more crucial.Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct,those measures may have a direct impact on the performance of selected attributes in reduct.However,most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective,which are insufficient to identify attributes with superior learning performance,such as stability and accuracy.In order to improve the classification stability and classification accuracy of reduct,in this paper,a novel measure is proposed based on the fusion of supervised and unsupervised perspectives:(1)in terms of supervised perspective,approximation quality is helpful in quantitatively characterizing the relationship between attributes and labels;(2)in terms of unsupervised perspective,conditional entropy is helpful in quantitatively describing the internal structure of data itself.In order to prove the effectiveness of the proposed measure,18 University of CaliforniaIrvine(UCI)datasets and 2 Yale face datasets have been employed in the comparative experiments.Finally,the experimental results show that the proposed measure does well in selecting attributes which can provide distinguished classification stabilities and classification accuracies.

关键词： Approximation quality attribute reduction conditional entropy neighborhood rough set

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