Generation of invernal-valued fuzzy partitions in order to optimise IVFARC algorithm

Abstract

IVFARC is a classifier based on interval-valued fuzzy association rules. This classifier provides its knowledge to correctly predict the class of a given example from a model based on fuzzy association rules. They are fuzzy because they define the space of fuzzy subsets, which allow to obtain the information of the problem. The first part of the algorithm is to generate the intervalvalued rules, so is needed to obtain the interval-valued fuzzy partitions of the data. This task is performed by a genetic algorithm, however, it is computationally very expensive and therefore very slow. The goal is to eliminate this first part of the algorithm and replace it with other ideas that are not so computationally demanding. An idea is proposed to use clustering methods to try to see the trend of the data and to define the mentioned interval-valued fuzzy partitions. Keeping in mind that directly interval-valued fuzzy sets must be obtained, the first thing to do is to find the centroids/representatives of the data. In case there are n linguistic labels, should be sought n centroids. This latter will construct the fuzzy sets, then by repeating this process several times and unifying the executions, will articulate the interval-valued fuzzy sets. Along with the interval-valued concept, it helps to allow for the management of uncertainty in the data and gives more flexibility in the rules.