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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Kuncheva, L.I.: Fuzzy Classifier Design, vol. 49. Springer, Heidelberg (2000)
Zhang, Y., Wu, X.B., Xing, Z.Y., Hu, W.L.: On generating interpretable and precise fuzzy systems based on Pareto multi-objective cooperative co-evolutionary algorithm. Appl. Soft Comput. J. 11(1), 1284–1294 (2011)
Koshiyama, A.S., Vellasco, M.M.B.R., Tanscheit, R.: GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems. Appl. Soft Comput. 37, 561–571 (2015)
Lemos, A., Caminhas, W., Gomide, F.: Evolving Intelligent Systems: Methods, Algorithms and Applications, pp. 117–159. Springer, Berlin, Heidelberg (2013)
Paredes, J., Tanscheit, R., Vellasco, M., Koshiyama, A.: Automatic synthesis of fuzzy inference systems for classification. In: Carvalho, J.P., Lesot, M.-J., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2016. CCIS, vol. 610, pp. 486–497. Springer, Heidelberg (2016). doi:10.1007/978-3-319-40596-4_41
Ishibuchi, H., Nakashima, T., Nii, M.: Classification and modeling with linguistic information granules: advanced approaches to linguistic Data Mining (2006)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms (2004)
Panov, P., Džeroski, S.: Combining bagging and random subspaces to create better ensembles. In: International Conference on Intelligent Data Analysis, pp. 118–129 (2007)
Kleiner, A., Jordan, M.I.: The big data bootstrap. In: Proceedings of 29th International Conference Machine Learning, p. 8 (2012)
Fernández, A., Calderón, M., Barrenechea, E., Bustince, H., Herrera, F.: Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations. Fuzzy Sets Syst. 161(23), 3064–3080 (2010)
Calvo, T., Kolesárová, A., Komorníková, M., Mesiar, R.: Aggregation operators: properties, classes and construction methods. Aggreg. Oper. New Trends Appl. 97(1), 3–104 (2002)
Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml. Accessed 01 Mar 2016
del Río, S., López, V., Benítez, J.M., Herrera, F.: A mapreduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules. Int. J. Comput. Intell. Syst. 8(3), 422–437 (2015)
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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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