|Table of Contents|

Attribute Reduction Algorithms Based on Dominance-Equivalence Relations(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年03期
Page:
45-
Research Field:
·计算机科学·
Publishing date:

Info

Title:
Attribute Reduction Algorithms Based on Dominance-Equivalence Relations
Author(s):
Wu Tingting1; Li Yan1; Guo Nana1; He Qiang2
(1.College of Mathematics and Information Science,Hebei University,Key Lab. of Machine Learning and Computational Intelligence,Baoding 071002,China)(2.College of Science,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
Keywords:
rough set; dominance-equivalence relation; attributes reduction; discernibility matrix; sample pair
PACS:
TP311
DOI:
10.3969/j.issn.1001-4616.2017.03.007
Abstract:
Considering multiple criteria classification problems,dominance relations and equivalence relations can be respectively introduced to condition attributes and decision attributes to describe different types of data. Based on the dominance-equivalence relations,a novel attribute reduction method based on sample pair selection is developed to deal with this kind of information systems. Instead of calculating the whole discernibility matrix,the proposed method only store the useful attributes for attribute reduction by selecting the discerned sample pairs,and therefore it can significantly improve the time costin attribute reduction. In addition,we propose an approximate reduction algorithm in order to deal with comparative large-scale information systems. This algorithm add attributes based on attribute importance and it’s time saving. Finally,the experimental results on UCI data sets demonstrate the feasibility and effectiveness of the proposed algorithms.

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Last Update: 2017-09-30