[1]GAO Yuan,CHEN Xiangjian,WANG Pingxin,et al.Attribute reduction over consistent samples[J].CAAI Transactions on Intelligent Systems,2019,14(6):1170-1178.[doi:10.11992/tis.201905051]
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Attribute reduction over consistent samples

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1170-1178 Column: 学术论文—机器感知与模式识别 Public date: 2019-11-05
Title:
Attribute reduction over consistent samples
Author(s):
GAO Yuan1;  CHEN Xiangjian1;  WANG Pingxin2;  YANG Xibei1
1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, China;
2. School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Keywords:
attribute reduction; classification accuracy; clustering; consistent samples; ensemble; heuristic algorithm; neighborhood rough set; multiple criteria
CLC:
TP181
DOI:
10.11992/tis.201905051
Abstract:
As one of the key topics in rough sets theory, attribute reduction aims to remove redundant attributes in a data set according to a given constraint condition. Based on greedy strategy, the heuristic algorithm is an effective strategy in finding reductions. Traditional heuristic algorithms usually need to scan all samples in a data set to compute the significance of attributes to further obtain a reduction. However, different samples have different contributions to the process of computing significance. Some samples have little relation to the significance, and some even have no contribution to the significance. Therefore, scanning all samples to compute reductions may require too much time, and the time may be unacceptable if the number of samples is too large. To fill such a gap, we have proposed an attribute reduction algorithm with sample selection, which is based on the consistent principle. The algorithm is composed of three stages. First, the samples that satisfy the consistent principle were selected; second, a new decision system was constructed with these selected samples; finally, reductions were derived from the heuristic algorithm over the new decision system. Experimental results demonstrated that, compared with the attribute reduction algorithm with a cluster-based sample selection, our new algorithm can offer better classification accuracy.
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