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Erschienen in: Knowledge and Information Systems 2/2013

01.08.2013 | Regular Paper

An ensemble method for fuzzy rule-based classification systems

verfasst von: Basma Soua, Amel Borgi, Moncef Tagina

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2013

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Abstract

Fuzzy rule-based classification systems are very useful tools in the field of machine learning as they are able to build linguistic comprehensible models. However, these systems suffer from exponential rule explosion when the number of variables increases, degrading, therefore, the accuracy of these systems as well as their interpretability. In this article, we propose to improve the comprehensibility through a supervised learning method by automatic generation of fuzzy classification rules, designated SIFCO–PAF. Our method reduces the complexity by decreasing the number of rules and of antecedent conditions, making it thus adapted to the representation and the prediction of rather high-dimensional pattern classification problems. We perform, firstly, an ensemble methodology by combining a set of simple classification models. Subsequently, each model uses a subset of the initial attributes: In this case, we propose to regroup the attributes using linear correlation search among the training set elements. Secondly, we implement an optimal fuzzy partition thanks to supervised discretization followed by an automatic membership functions construction. The SIFCO–PAF method, analyzed experimentally on various data sets, guarantees an important reduction in the number of rules and of antecedents without deteriorating the classification rates, on the contrary accuracy is even improved.

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Fußnoten
1
SIFCO is acronyme of systéme d’Inférence floue avec corrélation (fuzzy inference system with correlation).
 
2
SIFCO–PAF is acronyme of système d’Inférence floue avec corrélation et partition floue supervisée (fuzzy inference system with correlation and fuzzy supervised partition).
 
3
In SIFCO–PAF, the number of fuzzy sets associated with each attribute may be different. Here, one can take for K the largest number of fuzzy sets.
 
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Metadaten
Titel
An ensemble method for fuzzy rule-based classification systems
verfasst von
Basma Soua
Amel Borgi
Moncef Tagina
Publikationsdatum
01.08.2013
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2013
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-012-0532-7

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