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On scalability of rough set methods. (English) Zbl 1211.68358
Hüllermeier, Eyke (ed.) et al., Information processing and management of uncertainty in knowledge-based systems. Theory and methods. 13th international conference, IPMU 2010, Dortmund, Germany, June 28–July 2, 2010. Proceedings. Part I. Berlin: Springer (ISBN 978-3-642-14054-9/pbk; 978-3-642-14055-6/ebook). Communications in Computer and Information Science 80, 288-297 (2010).
Summary: This paper presents some recent results of the research on the scalability of rough set based classification methods. The proposed solution is based on the close relationship between reduct calculation problem in rough set theory and association rule generation problem. In this paper, the set of decision rules satisfying the test object is generated directly from the training data set. To make it scalable, we adopted the idea of the FP-growth algorithm for frequent item-sets. The experimental results on some benchmark data sets are showing the ability of the proposed solution to process growing data sets.
For the entire collection see [Zbl 1200.68010].
MSC:
68T10 Pattern recognition, speech recognition
68P05 Data structures
68T30 Knowledge representation
Software:
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