Rough-fuzzy set theoretic approach to evaluate the importance of input features in classification

Artificial neural networks are currently employed to capture the reasoning process involved in bidding a hand in a Contract Bridge game. Input hand in a Bridge game is conveniently represented as a series of 52 one/zero, where presence or absence of a card is denoted by 1 or 0. In this input representation all the cards, that are present in a hand, receive equal importance. Since the class discriminatory property of all the cards are not same to classify an input hand, the representation of each input pattern however should be biased based on the importance of each card. This necessitates a way to measure the importance of each card. The notion of rough set can be effectively exploited to determine the importance of each feature from this incomplete knowledge. Moreover, the classification task involved in bidding is inherently fuzzy. Hence, in this paper a rough-fuzzy set based measure is proposed to evaluate the importance of each feature. The efficacy of the proposed scheme is demonstrated by some experimental results