COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules

Abstract

This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with the best cooperation. Our proposal has shown good results in solving three different applications when compared to other methods.

COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy

“…A family of efficient and simple methods to derive fuzzy rules guided by covering criteria of the data in the example set, called ad hoc data-driven methods, has been proposed in the literature in the last few years [3]. Their high performance, in addition to their quickness and easy understanding, make them very suitable for learning tasks.…”

“…With the aim of addressing these drawbacks keeping the interesting advantages of ad hoc data-driven methods, a new methodology to improve the accuracy obtaining better cooperation among the rules is proposed in [3]: the COR methodology. Instead of selecting the consequent with the highest performance in each subspace like ad hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the FRBS to be more accurate thanks to having a KB with better cooperation.…”

“…In [3], accurate linguistic models have been obtained using simulated annealing. Other techniques such as tabu search, genetic algorithms, and ant colony optimization (ACO) have been also applied to COR methodology [4].…”

“…In [3], successful linguistic models have been obtained using simulated annealing. Nevertheless, these results could be improved incorporating heuristic information to the learning process.…”

Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models

“…-Weighted linguistic rule learning [7,21,25,35]: It is based on including an additional parameter for each rule that indicates its importance degree in the inference process, instead of considering all rules equally important as in the usual case. -Rule cooperation [3,31]: This approach follows the primary objective of inducing a better cooperation among the linguistic rules. To do so, the rule base design is made using global criteria that jointly consider the action of the different rules.…”