A class discriminality measure based on feature space partitioning

-This paper presents a new class discriminability measure based on an adaptive partitioning of the feature space according to the available class samples. It is intended to be used as a criterion in a classifier-independent feature selection procedure. The partitioning is performed according to a binary splitting rule and appropriate stopping criteria. Results from several tests with Gaussian and non-Gaussian, multidimensional and multiclass computer-generated samples, were very similar to those obtained using a Bayes error criterion function, i.e. the optimal feature subsets selected by both criterion functions-were the same. The main advantage of the new measure is that it is computationally efficient. Class discriminability measure Feature selection criterion function Variable selection criterion Feature evaluation Interclass distance measure Class separability measure