Journal
KNOWLEDGE-BASED SYSTEMS
Volume 73, Issue -, Pages 81-96Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2014.09.008
Keywords
Rough sets theory; Probabilistic rough sets; Incremental learning; Updating approximations; Knowledge discovery
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Funding
- National Science Foundation of China [71201133, 61175047, 71090402/G1]
- Youth Social Science Foundation of the Chinese Education Commission [11YJC630127]
- Research Fund for the Doctoral Program of Higher Education of China [20120184120028]
- China Postdoctoral Science Foundation [2012M520310, 2013T60132]
- Fundamental Research Funds for the Central Universities of China [SWJTU12CX117]
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The attribute set in an information system evolves in time when new information arrives. Both lower and upper approximations of a concept will change dynamically when attributes vary. Inspired by the former incremental algorithm in Pawlak rough sets, this paper focuses on new strategies of dynamically updating approximations in probabilistic rough sets and investigates four propositions of updating approximations under probabilistic rough sets. Two incremental algorithms based on adding attributes and deleting attributes under probabilistic rough sets are proposed, respectively. The experiments on five data sets from UCI and a genome data with thousand attributes validate the feasibility of the proposed incremental approaches. (C) 2014 Elsevier B.V. All rights reserved.
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