Abstract
This work describes the use of a recently proposed technique – gene expression programming – for knowledge discovery in the data mining task of data classification. We propose a new method for rule encoding and genetic operators that preserve rule integrity, and implemented a system, named GEPCLASS. Due to its encoding scheme, the system allows the automatic discovery of flexible rules, better fitted to data. The performance of GEPCLASS was compared with two genetic programming systems and with C4.5, over four data sets in a five-fold cross-validation procedure. The predictive accuracy for the methods compared were similar, but the computational effort needed by GEPCLASS was significantly smaller than the other. GEPCLASS was able to find simple and accurate rules as it can handle continuous and categorical attributes.
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© 2006 Springer-Verlag Berlin Heidelberg
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Weinert, W.R., Lopes, H.S. (2006). GEPCLASS: A Classification Rule Discovery Tool Using Gene Expression Programming. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_95
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DOI: https://doi.org/10.1007/11811305_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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