Abstract
In this paper we compare two algorithms that are capable of generating fuzzy partitions from data so as to verify a number of interpretability constraints: Hierarchical Fuzzy Partitioning (HFP) and Double Clustering with A* (DC*). Both algorithms exhibit the distinguishing feature of self-determining the number of fuzzy sets in each fuzzy partition, thus relieving the user from the selection of the best granularity level for each input feature. However, the two algorithms adopt very different approaches in generating fuzzy partitions, thus motivating an extensive experimentation to highlight points of strength and weakness of both. The experimental results show that, while HFP is on the average more efficient, DC* is capable of generating fuzzy partitions with a better trade-off between interpretability and accuracy, and generally offers greater stability with respect to its hyper-parameters.
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References
Mencar, C.: Interpretability of Fuzzy Information Granules. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing. SCI, vol. 182, pp. 95–118. Springer, Heidelberg (2009)
Mencar, C., Fanelli, A.M.: Interpretability constraints for fuzzy information granulation. Information Sciences 178, 4585–4618 (2008)
Guillaume, S., Charnomordic, B.: Generating an Interpretable Family of Fuzzy Partitions From Data. IEEE Transactions on Fuzzy Systems 12(3), 324–335 (2004)
Castellano, G., Fanelli, A.M., Mencar, C., Plantamura, V.L.: Classifying data with interpretable fuzzy granulation. In: Proceedings of the 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems 2006, Tokyo, Japan, pp. 872–877 (2006)
Guillaume, S., Charnomordic, B.: Learning interpretable fuzzy inference systems with FisPro. Information Sciences 181(20), 4409–4427 (2011)
Alonso, J.M., Magdalena, L.: Generating Understandable and Accurate Fuzzy Rule-Based Systems in a Java Environment. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds.) WILF 2011. LNCS, vol. 6857, pp. 212–219. Springer, Heidelberg (2011)
Kohonen, T.: Self-organizing maps. Information Sciences, vol. 30. Springer (2001)
Edelkamp, S., Schrödl, S.: Heuristic Search: Theory and Applications. Morgan Kaufmann (2011)
Mencar, C., Consiglio, A., Castellano, G., Fanelli, A.M.: Improving the Classification Ability of DC* Algorithm. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 145–151. Springer, Heidelberg (2007)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
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Lucarelli, M., Castiello, C., Fanelli, A.M., Mencar, C. (2013). Automatic Design of Interpretable Fuzzy Partitions with Variable Granularity: An Experimental Comparison. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_29
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DOI: https://doi.org/10.1007/978-3-642-38658-9_29
Publisher Name: Springer, Berlin, Heidelberg
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