Introduction

Fuzzy pattern recognition is sometimes identified with fuzzy clustering or with fuzzy if-then systems used as classifiers. In this book we adopt a broader view: fuzzy pattern recognition is about any pattern classification paradigm that involves fuzzy sets. To a certain extent fuzzy pattern recognition is dual to classical pattern recognition, as delineated in the early seventies by Duda and Hart [87], Fukunaga [100], Tou and Gonzalez [324], and thereby consists of three basic components: clustering, classifier design and feature selection [39] . Fuzzy clustering has been the most successful offspring of fuzzy pattern recognition so far. The fuzzy c-means algorithm devised by Bezdek [34] has admirable popularity in a great number of fields, both engineering and non-engineering. Fuzzy feature selection is virtually absent, or disguised as something else. This book is about the third component fuzzy classifier design.