Abstract
One of the main advantages of fuzzy classifier models is their linguistic interpretability, revealing the relation between input variables and the output class. However, these systems suffer from the curse of dimensionality when dealing with high dimensional problems (large number of attributes and instances). This paper presents a new fuzzy classifier model, named RandomFIS, that provides good performance in both classification accuracy and rule base interpretability even when dealing with databases comprising large numbers of inputs (attributes) and patterns (instances). RandomFIS employs concepts from Random Subspace and Bag of little Bootstrap (BLB), resulting in an ensemble of fuzzy classifiers. It was tested with different classification benchmarks, proving to be an accurate and interpretable model, even for problems involving big databases.
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Samudio, O., Vellasco, M., Tanscheit, R., Koshiyama, A. (2017). RandomFIS: A Fuzzy Classification System for Big Datasets. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_27
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DOI: https://doi.org/10.1007/978-3-319-47898-2_27
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