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A genetic learning of the fuzzy rule-based classification system granularity for highly imbalanced data-sets

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3 Author(s)
Villar, P. ; Dept. of Software Eng., Univ. of Granada, Granada, Spain ; Fernández, A. ; Herrera, F.

In this contribution we analyse the significance of the granularity level (number of labels) in Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to adapt the number of fuzzy labels for each problem, applying a fine granularity in those variables which have a higher dispersion of values and a thick granularity in the variables where an excessive number of labels may result irrelevant. We compare this methodology with the use of a fixed number of labels and with the C4.5 decision tree.

Published in:

Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on

Date of Conference:

20-24 Aug. 2009