An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets
Identificadores
URI: https://hdl.handle.net/10481/77897Metadata
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Springer
Materia
Fuzzy rule based classification systems Over-sampling Imbalanced Data-sets Rule weight Fuzzy Reasoning Method Inteligencia artificial Artificial intelligence
Date
2007Referencia bibliográfica
Published version: Fernández, A... [et al.] (2007). An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-540-73400-0_21]
Sponsorship
Spanish Projects TIN-2005-08386-C05-01 & TIC-2005-08386- C05-03Abstract
In this contribution we carry out an analysis of the rule
weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification
Systems in the framework of imbalanced data-sets with a high
imbalance degree. We analyze the behaviour of the Fuzzy Rule Based
Classification Systems searching for the best configuration of rule weight
and Fuzzy Reasoning Method also studying the cooperation of some
pre-processing methods of instances. To do so we use a simple rule base
obtained with the Chi (and co-authors’) method that extends the wellknown
Wang and Mendel method to classification problems.
The results obtained show the necessity to apply an instance preprocessing
step and the clear differences in the use of the rule weight
and Fuzzy Reasoning Method.
Finally, it is empirically proved that there is a superior performance
of Fuzzy Rule Based Classification Systems compared to the 1-NN and
C4.5 classifiers in the framework of highly imbalanced data-sets.