Abstract
In this paper a hybrid classifier construction using rough sets and fuzzy logic is presented. Nowadays, we tackle with many realistic multi-dimensional problems with continuous values and overlaps in the feature space which require sophisticated recognition algorithms. Many methods have been proposed in the literature to improve classification accuracy, but it is increasingly harder to build new classifier from the scratch. Instead, new fusion methods are proposed to overcome this problem. In our rough-fuzzy approach data pre-processing and crisp discretization have a significant impact on the final classification efficiency. To deal with the problem of finding the optimal cuts in the feature space a genetic algorithm was proposed. After the algorithm description, in this paper also simulation investigations using different datasets from UCI Machine Learning Repository are presented.
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Zolnierek, A., Majak, M. (2013). Hybrid Approach Using Rough Sets and Fuzzy Logic to Pattern Recognition Task. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_42
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DOI: https://doi.org/10.1007/978-3-642-40846-5_42
Publisher Name: Springer, Berlin, Heidelberg
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