Fuzzy rule selection using Iterative Rule Learning for speech data classification

Fuzzy rule-based systems have been successfully used for pattern classification. These systems focus on generating a rule-base from numerical input data. The resulting rule-base can be applied on classification problems. However, we are faced with some challenges when generating and selecting the appropriate rules to create final rule-base. In this paper, a novel approach for rule selection is proposed. The proposed algorithm makes the use of iterative rule learning (IRL) to reduce the search space of the classification problem in hand for rule-base extraction. The major element of our proposed approach is an evaluation metric which is able to accurately estimate the degree of cooperation of the candidate rule with current rules in the rule-base. Finally, fine-tuning of the selected rules is handled by employing a proposed rule-weighting mechanism. To evaluate the performance of the proposed scheme, TIMIT speech corpus was utilized for framewise classification of speech data. The results show the effectiveness of the proposed method while preserving the interpretability of the classification results.