Adaptive strategies of selecting feature subsets in pattern recognition

A sequential decision model is discussed to design adaptive receptor for selecting feature subsets in recognition systems. Selection strategies are proposed to choose subsets of features based on the classification results fed back from the classifier. These strategies are adaptive in the sense that the selection is constantly guided by the feedback results in such a way as to maximize the long-run proportion of correct recognition. A character recognition experiment was carried out on a computer-simulated basis to demonstrate the feasibility of this model.