Abstract
Classification is an important task widely researched by the machine learning and fuzzy communities. In this paper, we present and compare methods from both communities, in order to support the selection of a suitable method, according to two conflicting objectives: accuracy × interpretability. Two groups of rule-based methods are analysed: decision tree-based and genetic-based approaches. For the tree-based approaches, C4.5, PART and FuzzyDT, a fuzzy version of the C4.5 algorithm, are used. For the genetic-based approaches, MPLCS, a method from the machine learning community to generate rule-based models, SLAVE and FCA-Based, both fuzzy-based, are analysed. Since accuracy and interpretability are usually conflicting objectives, in this paper, we briefly present these methods and then discuss the models generated by them. Comparisons take into account the error rates and syntactic complexity of the produced models. Ten benchmark datasets are used in the experiments with a 10 fold cross-validation strategy. Results show that FCA-Based and MPLCS are able to obtain good accuracy and interpretability.
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Cintra, M.E., Monard, M.C., Camargo, H.A. (2013). On Rule Learning Methods: A Comparative Analysis of Classic and Fuzzy Approaches. In: Yager, R., Abbasov, A., Reformat, M., Shahbazova, S. (eds) Soft Computing: State of the Art Theory and Novel Applications. Studies in Fuzziness and Soft Computing, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34922-5_7
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