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.
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, Tiergartenstraße 17, 69121 Heidelberg, Germany