Classification systems have been widely applied in different fields such as medical diagnosis. A fuzzy rule-based classification system (FRBCS) is one of the most popular approaches used in pattern classification problems. One advantage of a fuzzy rule-based system is its interpretability. However, we're faced with some challenges when generating the rule-base. In high dimensional problems, we can not generate every possible rule with respect to all antecedent combinations. In this paper, by making the use of some data mining concepts, we propose a method for rule generation, which can result in a rule-base containing rules of different lengths. Then, our rule learning algorithm based on R.O.C analysis tunes the rule-base to have better classification ability. Our goal in this article, is to check if generating cooperative rule-bases containing rules of different dimensions, can lead to better generalization ability. To evaluate the performance of the proposed method, a number of UCI-ML data sets were used. The results show that considering cooperation in a rule-base tuned by rule weighting process can improve the classification accuracy. It is also shown that increasing the maximum length of rules in the initial rule-base, improves the classification accuracy.
['Omid Dehzangi', 'Mansoor J. Zolghadri', 'Shahram Taheri', 'Seyyed Mostafa Fakhrahmad']
Efficient Fuzzy Rule Generation: A New Approach Using Data Mining Principles and Rule Weighting