Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers

Abstract

The use of fuzzy quantifiers in linguistic fuzzy models helps to build fuzzy systems that use linguistic terms in a more natural way. Although several fuzzy quantification techniques have been developed, the application of the existing techniques seems very limited. This paper proposes an application of fuzzy quantification to replace crisp weights in subsethood-based fuzzy rule models. I n addition to the concern that fuzzy models should have high accuracy rate, attention has also been taken t o maintain the simplicity of the generated fuzzy model. The objective is to produce quantifier-based fuzzy models which are not only readable but also practically applicable. T h e quantifier based fuzzy model is then applied to classification tasks. The classification accuracies of fuzzy models that use crisp weights, continuous quantifiers, multi-valued quantifiers and two-valued quantifiers are compared. Experimental results show that the classification accuracy of the fuzzy model that uses continuous quantifiers is: 1) as good as the classification accuracy of the fuzzy models that use crisp weights, and 2) in most cases, better than fuzzy models that use multi-valued quantifiers or two-valued quantifiers.

A quantifier-based fuzzy classification system for breast cancer patients

“…This paper has presented a data-driven subsethood-based fuzzy rule induction algorithm, fuzzyQSBA [34] and its application to a breast cancer dataset. The results show that the model is able to categorise patients into the seven treatment groups previously identified [35] and demonstrate that the final classification indeed meets the initial algorithm requirements and specifications.…”

“…However, in the rest of the paper we will use the definition reported in equation (1) as our goal is to extend the fuzzyQSBA algorithm [34].…”

“…The resulting new method is called fuzzyQSBA [34] and the induced ruleset can computationally be represented by…”

“…The data-driven subsethood-based fuzzy rule induction algorithm, fuzzyQSBA [34] was used to determine the fuzzy class membership rules. In our particular case, the sets A and D of equation (2) are the set of fuzzified data and the set of classification outcomes, respectively.…”

“…The purpose of this paper is to use a data-driven subsethood-based fuzzy rule induction algorithm, named 'fuzzy quantification subsethood-based algorithm' (fuzzyQSBA) [34] to refine previously identified breast cancer treatment groups [35]. In addition, using a rule simplification technique, a linguistic ruleset can be extracted from the algorithm.…”