Abstract
A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. In this paper, a new approach using class-based partitioning is proposed to improve the performance of genetic-based classifiers. Rules are defined with fuzzy genes to represent variable length rules. We experimentally evaluate our approach on four different data sets and demonstrate that our algorithm can improve classification rate compared to normal Rule-based classification GAs [1] with non-partitioned techniques.
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El-Kafrawy, P., Sauber, A. (2011). Using Class Decomposition for Building GA with Fuzzy Rule-Based Classifiers. In: Mehrotra, K.G., Mohan, C., Oh, J.C., Varshney, P.K., Ali, M. (eds) Developing Concepts in Applied Intelligence. Studies in Computational Intelligence, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21332-8_10
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DOI: https://doi.org/10.1007/978-3-642-21332-8_10
Publisher Name: Springer, Berlin, Heidelberg
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