Fuzzy-rough neural networks for vowel classification

In many real life applications two patterns from the same cluster belong to different classes, and hence, classification based on mere similarity property is inadequate. This problem arises because the available features are not sufficient to discriminate the classes. It implies that the fuzzy clusters generated by the input features have rough uncertainty. This paper proposes a fuzzy-rough set based network which exploits fuzzy-rough membership functions to reduce this problem. The proposed network is theoretically a powerful classifier as it is equivalent to a universal approximator. Moreover, its activity is transparent as it can easily be mapped to a Takagi-Sugeno type fuzzy rule base system. The efficacy of the proposed method is studied on a vowel recognition problem.