Abstract
The theory of rough set provides a formal tool for knowledge discovery from imprecise and incomplete data. Inducing rules from datasets is one of the main tasks in rough set based data mining. According to Occam Principle, the most ideal decision rules should be the simplest ones. Unfortunately, induction of minimal decision rules turns out to be a NP-hard problem. In this paper, we propose an heuristic minimal decision rules induction algorithm RuleIndu whose time complexity is O(| A| * | U |2) and space requirement is O(| A| * | U |). In order to investigate the efficiency of proposed algorithm, we provide the comparison between our algorithm RuleIndu and some other rules induction algorithms on some problems from UCI repository. In most cases, our algorithm RuleIndu outmatches some other rules induction algorithms not only in classification time but also in classification accuracy.
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© 2004 Springer-Verlag Berlin Heidelberg
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Tan, S., Gu, J. (2004). An Efficient Rules Induction Algorithm for Rough Set Classification. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_28
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DOI: https://doi.org/10.1007/978-3-540-30214-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23357-2
Online ISBN: 978-3-540-30214-8
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