[1]CHENG Long,QIAN Wenbin,WANG Yinglong,et al.An incremental attribute reduction algorithm for incomplete decision system with weak labeling[J].CAAI Transactions on Intelligent Systems,2020,15(6):1079-1090.[doi:10.11992/tis.202001017]
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An incremental attribute reduction algorithm for incomplete decision system with weak labeling
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 6
Page number:
1079-1090
Column:
学术论文—知识工程
Public date:
2020-11-05
- Title:
- An incremental attribute reduction algorithm for incomplete decision system with weak labeling
- Keywords:
- attribute reduction; rough set; discernibility pair; mixed data; incremental learning; semi-supervised learning; relative importance; dynamic data
- CLC:
- TP391
- DOI:
- 10.11992/tis.202001017
- Abstract:
- Due to the high cost of data annotation and dynamic change of data, many practical applications have a lot of incomplete data with weak labeling. In view of the above complex scenarios, based on the theory of granular computing, the concept of discernibility pairs of incomplete data is proposed and provides a measurement method for the relative importance of attributes. The attribute reduction algorithm is designed for an incomplete decision system with weak labeling, which can reduce the search space and improve the efficiency of attribute reduction. Besides, the dynamic updating mechanism of attribute reduction is analyzed based on the dynamic change of instances. In this study, an incremental attribute reduction algorithm is designed under a semi-supervised scene, and the experimental results show the feasibility and effectiveness of the proposed algorithm.
- References:
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Last Update:
2020-12-25