A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Fuzzy set theory has been widely and successfully used as a mathematical tool to combine the outputs provided by the individual classifiers in a multiclassification system by means of a fuzzy aggregation operator. However, to the best of our knowledge, no fuzzy combination method has been proposed, which is composed of a fuzzy rule-based system. We think this can be a very promising research line as it allows us to benefit from the key advantage of fuzzy systems, i.e., their interpretability. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure, and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow standard fuzzy classifiers to deal with high-dimensional problems, avoiding the curse of dimensionality, as the chance to perform classifier selection at class level is also incorporated, into the method. We conduct comprehensive experiments considering 20 UCI datasets with different dimensionality, where our approach improves or at least maintains accuracy, while reducing complexity of the system, and provides some interpretability insight into the multiclassification system reasoning mechanism. The results obtained show that this approach is able to compete with the state-of-the-art multiclassification system selection and fusion methods in terms of accuracy, thus providing a good interpretability-accuracy tradeoff.
classifier selection interpretability-accuracy tradeoff bagging classifier fusion fuzzy rule-based multiclassification systems genetic fuzzy systems linguistic selection and fusion of individual classifiers