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作者:

Sun, Lin (Sun, Lin.) | Xu, Jiucheng (Xu, Jiucheng.) | Tian, Yun (Tian, Yun.)

收录:

EI Scopus SCIE

摘要:

Feature selection in large, incomplete decision systems is a challenging problem. To avoid exponential computation in exhaustive feature selection methods, many heuristic feature selection algorithms have been presented in rough set theory. However, these algorithms are still time-consuming to compute. It is therefore necessary to investigate effective and efficient heuristic algorithms. In this paper, rough entropy-based uncertainty measures are introduced to evaluate the roughness and accuracy of knowledge. Moreover, some of their properties are derived and the relationships among these measures are established. Furthermore, compared with several representative reducts, the proposed reduction method in incomplete decision systems can provide a mathematical quantitative measure of knowledge uncertainty. Then, a heuristic algorithm with low computational complexity is constructed to improve computational efficiency of feature selection in incomplete decision systems. Experimental results show that the proposed method is indeed efficient, and outperforms other available approaches for feature selection from incomplete and complete data sets. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词:

Feature selection Rough entropy Conditional entropy Incomplete decision system Rough set theory

作者机构:

  • [ 1 ] [Sun, Lin]Henan Normal Univ, Coll Comp & Informat Technol, Xinxiang 453007, Henan, Peoples R China
  • [ 2 ] [Xu, Jiucheng]Henan Normal Univ, Coll Comp & Informat Technol, Xinxiang 453007, Henan, Peoples R China
  • [ 3 ] [Sun, Lin]Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China
  • [ 4 ] [Tian, Yun]Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China

通讯作者信息:

  • [Xu, Jiucheng]Henan Normal Univ, Coll Comp & Informat Technol, Xinxiang 453007, Henan, Peoples R China

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来源 :

KNOWLEDGE-BASED SYSTEMS

ISSN: 0950-7051

年份: 2012

卷: 36

页码: 206-216

8 . 8 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

JCR分区:1

中科院分区:2

被引次数:

WoS核心集被引频次: 118

SCOPUS被引频次: 130

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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