Journal
INFORMATION SCIENCES
Volume 257, Issue -, Pages 210-228Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.09.035
Keywords
Knowledge discovery; Rough set; Set-valued ordered decision system; Incremental learning
Categories
Funding
- National Science Foundation of China [61175047, 61100117, 71201133]
- NSAF [U1230117]
- Youth Social Science Foundation of the Chinese Education Commission [11YJC630127]
- Fundamental Research Funds for the Central Universities [SWJTU11ZT08, SWJTU12CX091, SWJTU12CX117]
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Set-valued information systems are important type of data tables and generalized models of single-valued information systems. Approximations are the focal point of approaches to knowledge discovery based on rough set theory, which can be used to extract and represent the hidden knowledge in the form of decision rules. Attribute generalization refers to dynamic change of the attribute set in an information system with respect to the requirements of real-life applications. In this paper, we focus on maintaining approximations dynamically in set-valued ordered decision systems under the attribute generalization. Firstly, a matrix-based approach for computing approximations of upward and downward unions of decision classes is constructed by introducing the dominant and dominated matrices with respect to the dominance relation. Then, incremental approaches for updating approximations are proposed, which involves several modifications to relevant matrices without having to retrain from the start on all accumulated training data. Finally, comparative experiments on data sets from UCI as well as synthetic data sets show the proposed incremental updating methods are efficient and effective for dynamic attribute generalization. (C) 2013 Elsevier Inc. All rights reserved.
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