Framewise Phone Classification Using Weighted Fuzzy Classification Rules

Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base, and second, a weight is assigned to each training example as the cost of misclassification of it using the class distribution of its neighbors. Then, instead of minimizing the error-rate, the learning algorithm is modified to minimize the sum of costs for misclassified examples. Using six data sets from UCI-ML repository and the TIMIT speech corpus for frame wise phone classification, we show that our proposed algorithm considerably improves the prediction ability of the classifier.