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A study of heuristic evaluation measures in fuzzy rule induction

Afifi, Ashraf

Authors

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Dr Ashraf Afifi Ashraf.Afifi@uwe.ac.uk
Senior Lecturer in Engineering Management



Contributors

Lazaros Iliadis
Editor

Ilias Maglogiannis
Editor

Vassilis Plagianakos
Editor

Abstract

The rule induction process could be conceived as a search process,and hence an evaluation metric is needed to estimate the quality of rules found in the search space and to direct the search towards the best rule. The evaluation
measure is the most influential inductive bias in rule learning. It is therefore important to investigate its influence on the induction process and to compare the behaviour of different evaluation measures. Many different evaluation measures have been used to score crisp rules. For some of these measures, fuzzy variations have been designed and used to score fuzzy rules. This paper examines the most popular crisp evaluation measures and demonstrates how they can be adapted into the fuzzy domain. The paper also studies the performance of these measures on a large number of data sets when used in a recently developed fuzzy rule induction algorithm. Results show that there are no universally applicable evaluation measures and the choice of the best measure depends on the type of the data set and the learning problem.

Citation

Afifi, A. A. (2018). A study of heuristic evaluation measures in fuzzy rule induction. In L. Iliadis, I. Maglogiannis, & V. Plagianakos (Eds.), Artificial Intelligence Applications and Innovations, 533-545. Springer, Tiergartenstra├če 17, 69121 Heidelberg, Germany

Conference Name 14th Int. Conf. on Artificial Intelligence Applications and Innovations (AIAI-2018)
Start Date May 25, 2018
End Date May 27, 2018
Acceptance Date May 25, 2018
Publication Date May 22, 2018
Peer Reviewed Peer Reviewed
Pages 533-545
Series Title IFIP Advances in Information and Communication Technology
Book Title Artificial Intelligence Applications and Innovations
ISBN 9783319920061
Keywords fuzzy rule induction, heuristic evaluation measures, fuzzy sets
Public URL https://uwe-repository.worktribe.com/output/867892
Publisher URL https://doi.org/10.1007/978-3-319-92007-8
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here: https://doi.org/10.1007/978-3-319-92007-8
Title of Conference or Conference Proceedings : 14th Int. Conf. on Artificial Intelligence Applications and Innovations (AIAI-2018)

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