Summary
We discuss the problem of classification of imbalanced and overlapping classes. A fuzzy set approach is presented first – the classes are recognized using a fuzzy classifier. Next, we use intuitionistic fuzzy sets (A-IFSs, for short) to represent and deal with the same data. We show that the proposed intuitionistic fuzzy classifier has an inherent tendency to deal efficiently with imbalanced and overlapping data. We explore in detail the evaluation of the classifier results (especially from the point of view of recognizing the smaller class). We show on a simple example the advantages of the intuitionistic fuzzy classifier. Next, we illustrate its desirable behavior on a benchmark example (from UCI repository).
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Szmidt, E., Kukier, M. (2008). Atanassov’s Intuitionistic Fuzzy Sets in Classification of Imbalanced and Overlapping Classes. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds) Intelligent Techniques and Tools for Novel System Architectures. Studies in Computational Intelligence, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77623-9_26
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DOI: https://doi.org/10.1007/978-3-540-77623-9_26
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