A New Rule-weight Learning Method based on Gradient Descent

In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. In order to optimize the generalization accuracy, we use ruleweight as a simple mechanism to tune the classifier and propose a new learning method to iteratively adjust the weight of fuzzy rules. The rule-weights in the proposed method are derived by solving the minimization problem through gradient descent. Through computer simulations on some data sets from UCI repository, the proposed scheme shows a uniformly good behavior and achieves results which are comparable or better than other fuzzy and non-fuzzy classification systems, proposed in the past.