Abstract
Evolutionary-based methods provide a framework for mining classification rules, that is, rules that can be used to discriminate between data organized in several classes. In this paper, we propose a novel multi-objective extension for the standard Pittsburg approach. Key features of our model include (a) variable length chromosomes, implemented using an active bit string (mask), and (b) fitness evaluation and selection based on restricted non-dominated tournaments. Extensive numerical simulations show that the proposed algorithm is competitive with – and indeed outperforms in some cases – other well-known machine learning tools using benchmark datasets.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kshetrapalapuram, K.K., Kirley, M. (2005). Mining Classification Rules Using Evolutionary Multi-objective Algorithms. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_135
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DOI: https://doi.org/10.1007/11553939_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
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