NDLI: A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers

Content Provider	IEEE Xplore Digital Library
Author	Trawinski, K. Cordon, O. Sanchez, L. Quirin, A.
Copyright Year	1993
Abstract	Fuzzy set theory has been widely and successfully used as a mathematical tool to combine the outputs provided by the individual classifiers in a multiclassification system by means of a fuzzy aggregation operator. However, to the best of our knowledge, no fuzzy combination method has been proposed, which is composed of a fuzzy rule-based system. We think this can be a very promising research line as it allows us to benefit from the key advantage of fuzzy systems, i.e., their interpretability. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure, and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow standard fuzzy classifiers to deal with high-dimensional problems, avoiding the curse of dimensionality, as the chance to perform classifier selection at class level is also incorporated, into the method. We conduct comprehensive experiments considering 20 UCI datasets with different dimensionality, where our approach improves or at least maintains accuracy, while reducing complexity of the system, and provides some interpretability insight into the multiclassification system reasoning mechanism. The results obtained show that this approach is able to compete with the state-of-the-art multiclassification system selection and fusion methods in terms of accuracy, thus providing a good interpretability-accuracy tradeoff.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	950
Ending Page	965
Page Count	16
File Size	5382070
File Format	PDF
ISSN	10636706
Volume Number	21
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pragmatics Training Fuzzy systems Bagging Fuzzy reasoning Complexity theory Genetics linguistic selection and fusion of individual classifiers classifier fusion classifier selection fuzzy rule-based multiclassification systems genetic fuzzy systems interpretability–accuracy tradeoff
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Control and Systems Engineering Computational Theory and Mathematics

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