|Title||Some relationships between fuzzy and random set-based classifiers and models|
|Publication Type||Journal Article|
|Year of Publication||2002|
|Authors||Sánchez, Luciano, Casillas Jorge, Cordón O., and del Jesus M. J.|
|Journal||International Journal of Approximate Reasoning|
|Pagination||175 - 213|
|Keywords||Fuzzy classifiers, fuzzy models, Random set-based classifiers, Random set-based models|
When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known. In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.