Information Entropy and Information Granulation-based Uncertainty Measures in Incomplete Information Systems

Sun, Lin; Xu, Jiucheng; Xu, Tong

doi:10.12785/amis/080466

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In this section, we briefly review several basic concepts of rough sets, information entropy measures and neighborhood rough sets in [ 12 , 13 , 18 , 33 , 34 , 35 ].…”

Section: Previous Knowledgementioning

confidence: 99%

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Sun

Zhang

et al. 2019

Entropy

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Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

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“…In this section, we briefly review several basic concepts of rough sets, information entropy measures and neighborhood rough sets in [ 12 , 13 , 18 , 33 , 34 , 35 ].…”

Section: Previous Knowledgementioning

confidence: 99%

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Sun

Zhang

et al. 2019

Entropy

Self Cite

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“…Incompleteness in an information system is one of the main causes of uncertainty. RST, which is based on the traditional equivalence relation (i.e., reflexivity, symmetry, and transitivity) cannot directly deal with incomplete information systems, which greatly constrains the use of RST in practical applications (Gantayat et al 2014 ; Sun et al 2014 ). Hence, several extended models and methods for RST such as the tolerance relation (i.e., reflexivity, symmetry) (Wang and Zhang 2008 ), the asymmetric similarity relation (i.e., reflexivity, transitivity) (Stefanowski and Tsoukias 1999 ), the limited tolerance relation (i.e., reflexivity, symmetry) (Wang 2002 ), the dominance relation (reflexivity, transitivity) (Greco et al 2002 ; Hu et al 2012 ), and the general binary relation (i.e., reflexivity) (Yao 1998 ; Teng et al 2009 ; Zhu 2007 ) which can directly process an incomplete information system, have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Integrated measures for rough sets based on general binary relations

Teng

Liao²,

et al. 2016

SpringerPlus

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Uncertainty measures are important for knowledge discovery and data mining. Rough set theory (RST) is an important tool for measuring and processing uncertain information. Although many RST-based methods for measuring system uncertainty have been investigated, the existing measures cannot adequately characterise the imprecision of a rough set. Moreover, these methods are suitable only for complete information systems, and it is difficult to generalise methods for complete information systems to incomplete information systems. To overcome these shortcomings, we present new uncertainty measures, integrated accuracy and integrated roughness, that are based on general binary relations, and we study important properties of these measures. A theoretical analysis and examples show that the proposed integrated measures are more precise than existing uncertainty measures, they are suitable for both complete and incomplete information systems, and they are logically consistent. Therefore, integrated accuracy and integrated roughness overcome the limitations of existing measures. This research not only develops the theory of uncertainty, it also expands the application domain of uncertainty measures and provides a theoretical basis for knowledge acquisition in information systems based on general binary relations.

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Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

et al. 2018

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Information Entropy and Information Granulation-based Uncertainty Measures in Incomplete Information Systems

Cited by 3 publications

References 34 publications

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets

Integrated measures for rough sets based on general binary relations

Joint neighborhood entropy-based gene selection method with fisher score for tumor classification

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