[1]GAO Xueyi,ZHANG Nan,TONG Xiangrong,et al.Research on attribute reduction using generalized distribution preservation[J].CAAI Transactions on Intelligent Systems,2017,12(3):377-385.[doi:10.11992/tis.201704025]
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Research on attribute reduction using generalized distribution preservation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 377-385 Column: 学术论文—人工智能基础 Public date: 2017-06-25
Title:
Research on attribute reduction using generalized distribution preservation
Author(s):
GAO Xueyi1; 2;  ZHANG Nan1; 2;  TONG Xiangrong1; 2;  JIANG Lili1; 2
1. Key Lab for Data Science and Intelligent Technology of Shandong Higher Education Institutes, Yantai University, Yantai 264005, China;
2. School of Computer and Control Engineering, Yantai University, Yantai 264005, China
Keywords:
distribution preservation; attribute reduction; rough sets; probability distribution; discernibility matrix
CLC:
TP181
DOI:
10.11992/tis.201704025
Abstract:
Attribute reduction is a pertinent issue in rough set theory. Distribution reduction ensures that the probability distribution of each target does not change before and after reduction; i.e., it ensures that the confidence of every rule remains unchanged before and after reduction. In actual applications, people are often interested in rules that have higher or lower confidences. Thus, attribute reduction based on generalized distribution preservation is proposed in this paper. Confidences in [0, α] or [β, 1] were unchanged using the proposed technique. We also propose judgment methods for generalized-distribution-preservation attribute reduction and investigate the generalized attribute-reduction algorithm based on a discernibility matrix. Some special cases with respect to generalized-distribution-preservation attribute reduction are discussed in depth. Finally, experiments on four data sets downloaded from UCI show that some special cases with respect to generalized distribution preservation reduction could degenerate into some existing attribute reductions and inclusion relations exist in generalized distribution preservation attribute reduction under different confidence intervals, verifying the correctness of the relevant conclusions.
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