Abstract
This paper considers an evolutionary algorithm based on an information system for generating classification rules. Custom genetic operators and a multi-objective fitness function are designed for this representation. The approach has previously been illustrated using a binary class data set. In this paper we explore the possibility of using the algorithm on a multi-class data set. The accuracy of the rules produced by the evolutionary algorithm approach are compared to those obtained by a decision tree technique on the same data. The advantages of using an evolutionary classification technique over the more traditional decision tree structure are discussed.
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Bedingfield, S.E., Smith, K.A. (2003). Evolutionary Rule Generation Classification and Its Application to Multi-class Data. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44864-0_89
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DOI: https://doi.org/10.1007/3-540-44864-0_89
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