Abstract
Due to its predictive capacity and applicability in different fields, classification has been one of the most important tasks in data mining. In this task, the Perceptron Decision Trees (PDT) have been used with good results. Thus, this paper presents a Differential Evolution algorithm that evolves PDTs. Furthermore, we also present the concept of legitimacy which is used to reduce the costs of solution evaluation, a time consuming part of the algorithm. The experiments comparing our method with other seven well known classifiers, show that the proposed approach is competitive and has potential to build very accurate models. The best solutions found by it were the best ones in the majority of the tested databases.
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Lopes, R.A., Freitas, A.R.R., Silva, R.C.P., Guimarães, F.G. (2012). Differential Evolution and Perceptron Decision Trees for Classification Tasks. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_67
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DOI: https://doi.org/10.1007/978-3-642-32639-4_67
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