A classifier based on the maximal fuzzy similarity in the generalized Lukasiewicz-structure

The aim of this paper is to introduce improvements made to a classifier based on maximal fuzzy similarity. Improvements are based on the use of generalized Lukasiewicz-structure and weight optimization. The main benefits of the classifier are its computational efficiency and its strong mathematical background. It is based on many-valued logic and it provides semantic information about classification results. We show that if one chooses the power value in a right manner in the generalized Lukasiewicz-structure and the optimal weights for different feature, one can see significant enhancements in classification results