NDLI: An ensemble method for fuzzy rule-based classification systems

Content Provider	Springer Nature Link
Author	Soua, Basma Borgi, Amel Tagina, Moncef
Copyright Year	2012
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.
Starting Page	385
Ending Page	410
Page Count	26
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	36
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2012-09-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy rule-based classification systems Supervised learning Automatic generation of fuzzy rules Feature subset ensemble methodology Attributes regrouping Fuzzy supervised partition Information Systems and Communication Service Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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