[1]HU Jun,HUANG Xiaohan.Cost sensitive approximate attribute reduction for specific classes[J].CAAI Transactions on Intelligent Systems,2024,19(6):1468-1478.[doi:10.11992/tis.202309032]
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Cost sensitive approximate attribute reduction for specific classes

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1468-1478 Column: 学术论文—智能系统 Public date: 2024-12-05
Title:
Cost sensitive approximate attribute reduction for specific classes
Author(s):
HU Jun1; 2;  HUANG Xiaohan1; 2
1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 4000
Keywords:
rough set; uncertain Information; specific class; relative uncertainty; attribute importance; test-cost-sensitive; approximate attribute reduction; heuristic algorithm
CLC:
TP391
DOI:
10.11992/tis.202309032
Abstract:
Class-specific attribute reduction refers to reducing attributes that are provided specifically for a given decision class. Existing class-specific attribute reduction methods are often too strict, which limits their applicability in certain scenarios. For noisy data, this paper proposes a cost-sensitive approximate attribute reduction method tailored for specific classes. First, the method combines information from the positive and boundary regions to define the relative uncertainty for a specific class. Then, attribute importance is calculated using relative uncertainty and test cost, allowing for attribute selection based on importance and avoiding the inclusion of redundant attributes by relaxing the relative uncertainty. Finally, the study introduces a cost-sensitive approximate heuristic attribute reduction for specific classes. Experimental results show that the proposed method can maintain or even improve the reduction quality while achieving a more streamlined reduction compared to other methods, with a relatively lower test cost for the reduction set.
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