[1]YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(2):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
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An approach to attribute reduction combining attribute selection and deletion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 2 Page number: 183-186 Column: 学术论文—人工智能基础 Public date: 2013-04-25
Title:
An approach to attribute reduction combining attribute selection and deletion
Author(s):
YANG Chengdong1;  DENG Tingquan2
1. School of Informatics, Linyi University, Linyi 276005, China;
2. College of Science, Harbin Engineering University, Harbin 150001, China
Keywords:
discernibility matrix;  attribute reduction;  information redundancy;  artificial intelligence;  machine learning;  attribute selection;  attribute deletion
CLC:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201209056
Abstract:
Attribute reduction has been defined as a method for removing information redundancy effectively, which has been widely applied to artificial intelligence, and machine learning. However, an example demonstrates classical attribute reduction approaches based on discernibility matrix may not get a reduction with redundancy. Therefore, an attribute reduction based on discernibility matrix combining attribute selection and deletion was proposed and thus, the problem was solved effectively. Moreover, UCI standard data sets provide further explanations on the feasibility, effectiveness, and as well as additional information on reducing the number of attributes without the classical approaches.
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