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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A First Study on the Use of Interval-Valued Fuzzy Sets with Genetic Tuning for Classification with Imbalanced Data-Sets
verfasst von : J. Sanz, A. Fernández, H. Bustince, F. Herrera
Erschienen in: Hybrid Artificial Intelligence Systems
Verlag: Springer Berlin Heidelberg
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Classification with imbalanced data-sets is one of the recent challenging problems in Data Mining. In this framework, the class distribution is not uniform and the separability between the classes is often difficult. From the available techniques in the Machine Learning field, we focus on the use of Fuzzy Rule Based Classification Systems, as they provide an interpretable model for the end user by means of linguistic variables.
The aim of this work is to increase the performance of fuzzy modeling by adding a higher degree of knowledge by means of the use of Interval-valued Fuzzy Sets. Furthermore, we will contextualize the Interval-valued Fuzzy Sets with a post-processing genetic tuning of the amplitude of their upper bounds in order to enhance the global behaviour of this methodology.