FEATURE SELECTION FOR PATTERN RECOGNITION SYSTEMS

Abstract

The major problem of pattern recognition is essentially the discrimination of the input data between statistical populations via the search for features among members of a population. This chapter presents several approaches for the extraction of features in pattern recognition systems. The design of pattern recognition systems generally involves several major problem areas. The first problem is concerned with the representation of input data which can be measured from the objects of a pattern class. This is the sensing problem. The second problem is concerned with the selection of characteristic features or attributes from the received input data. This is often referred to as the feature extraction or selection problem. The third problem deals with the determination of optimum decision procedures which are needed in the process of identification and classification. This is the optimum decision problem. In solving the feature selection problem and the optimum decision problem, a set of parameters to be estimated and optimized is generally involved. This gives rise to the parameter estimation problem. The selection of features has been recognized as an important process in a pattern recognition system. When the complete set of discriminatory features for each pattern class can be determined from the measurement, the recognition and classification of the patterns will present no problem and automatic classification may be reduced to a simple matching procedure.