Fuzzy classification algorithm based on kernel covering and its application

This paper firstly introduces the basic concept of covering algorithm and kernel covering algorithm (KCA) adopting kernel function, then analyzes the influence of proximity principle used to judge rejection points on classifier´s effect. FKCA, i.e. Fuzzy Kernel Covering Algorithm, is proposed to improve the performance of classifier. The main improvement of FKCA is the change of radius selection and introduction of membership function. Also we discuss the influence of isolated covering and provide a method to reduce the number of coverings by the combination of second scanning with contribution calculation. Experiments show the performance gap between KCA and FKCA, and comparisons with other fuzzy classifiers are also performed. We apply FKCA to character recognition of car plates and the result is satisfactory.