A Sequential Decision Model for Selecting Feature Subsets in Pattern Recognition

This paper deals with a sequential decision model for selecting feature subsets in pattern recognition. Based upon this model, a number of selection strategies are proposed and their properties are investigated. The basic criterion of these selection strategies, which depend only on the last r (r finite) recognition samples, is to maximize the long-run proportion of correct recognition, the maximization being overall possible feature subsets. The sequential model is particularly suitable for the implementation of on-line selection processes often required in pattern recognition. A character recognition experiment has been simulated on a digital computer to demonstrate the feasibility of this approach.