Journal
FUZZY SETS AND SYSTEMS
Volume 258, Issue -, Pages 39-60Publisher
ELSEVIER
DOI: 10.1016/j.fss.2014.08.014
Keywords
Fuzzy rough sets; Incremental learning; Feature selection; Hybrid information systems; Big data
Funding
- National Science Foundation of China [61175047, 61100117]
- NSAF [U1230117]
- Youth Social Science Foundation of the Chinese Education Commission [11YJC630127]
- Scientific Research Fundation of Sichuan Provincial Education Department [13ZB0210]
- Fundamental Research Funds for the Central Universities [SWJTU11ZT08, SWJTU12CX117, SWJTU12CX091]
- Doctoral Innovation Funds of Southwest Jiaotong University [2013ZAP]
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In real-applications, there may exist many kinds of data (e.g., boolean, categorical, real-valued and set-valued data) and missing data in an information system which is called as a Hybrid Information System (HIS). A new Hybrid Distance (HD) in HIS is developed based on the value difference metric, and a novel fuzzy rough set is constructed by combining the HD distance and the Gaussian kernel. Considering the information systems often vary with time, the updating mechanisms for attribute reduction (feature selection) are analyzed with the variation of the attribute set. Fuzzy rough set approaches for incremental feature selection on HIS are presented. Then two corresponding incremental algorithms are proposed, respectively. Finally, extensive experiments on eight datasets from UCI and an artificial dataset show that the incremental approaches significantly outperform non-incremental approaches with feature selection in the computational time. (C) 2014 Elsevier B.V. All rights reserved.
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