Abstract
This paper focuses on problems related to learning rules using numerical data for the Hierarchical Fuzzy Logic Systems (HFLS) described in [8]. Using hierarchical structure of Fuzzy Logic Systems (FLS) some complex problems could be divided into subproblems with smaller dimensions. “Hierarchical” means that fuzzy sets produced as output of one of fuzzy logic systems are then processed as input of another as the sets of auxiliary variables. The main scope of this paper is to use HFLS in classification problems for different datasets from the UCI Machine Learning Repository (The UC Irvine Machine Learning Repository shared by Center for Machine Learning and Intelligent Systems (University of California, Irvine) available at https://archive.ics.uci.edu/ml/index.html). The proposal presented in this paper operates on a type-1 HFLS, built with the fuzzy logic systems (in the sense of Mamdani). Iris, Abalone, Wine, Wine Quality Red and White datasets were used. Obtained results are described and compared to other classification systems.
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Renkas, K., Niewiadomski, A. (2017). Hierarchical Fuzzy Logic Systems in Classification: An Application Example. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_27
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