Rule induction based on an incremental rough set

Authors:
Yu-Neng Fan;Tzu-Liang (Bill) Tseng;Ching-Chin Chern;Chun-Che Huang
Affiliations:
Department of Information Management, National Taiwan University, Taiwan No. 1, Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan, ROC;Department of Industrial Engineering, The University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968;Department of Information Management, National Taiwan University, Taiwan No. 1, Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan, ROC;Department of Information Management, National Chi Nan University, Taiwan, No. 1, University Road, Puli, Nantou 545, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule-extraction algorithm is proposed to resolve there aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.