Attribute reduction among decision tables by voting

Abstract According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.

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New algorithm for attribute reduction in inconsistent decision tables

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.00525 ◽

2008 ◽

Vol 28 (2) ◽

pp. 525-527

Author(s):

Xiao-yan WANG

Keyword(s):

Attribute Reduction ◽

Decision Tables

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On Finding All Reducts of Consistent Decision Tables

Cybernetics and Information Technologies ◽

10.1515/cait-2014-0001 ◽

2015 ◽

Vol 14 (4) ◽

pp. 3-10

Author(s):

Demetrovics Janos ◽

Vu Duc Thi ◽

Nguyen Long Giang

Keyword(s):

Data Processing ◽

Time Complexity ◽

Attribute Reduction ◽

The Other ◽

Decision Table ◽

Minimal Subset ◽

Reduction Problem ◽

Processing Information ◽

Decision Tables

Abstract The problem of finding reducts plays an important role in processing information on decision tables. The objective of the attribute reduction problem is to reject a redundant attribute in order to find a core attribute for data processing. The attribute reduction in decision tables is the process of finding a minimal subset of conditional attributes which preserve the classification ability of decision tables. In this paper we present the time complexity of the problem of finding all reducts of a consistent decision table. We prove that this time complexity is exponential with respect to the number of attributes of the decision tables. Our proof is performed in two steps. The first step is to show that there exists an exponential algorithm which finds all reducts. The other step is to prove that the time complexity of the problem of finding all reducts of a decision table is not less than exponential.

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