FEAST: Feature evaluation and selection technique for deployment in unsupervised nonparametric environments

Abstract

The problem of feature selection in a totally unsupervised, distribution free environment being conceptually ill-defined, the problem has been studied in an artifically evolved pseudosupervised environment. The evolution of such an environment is achieved by formulating a unified approach to the twin problems of feature selection and unsupervised learning. The solution of the latter problem leads to the pseudosupervised environment in which the features are evaluated by employing a multistate-choice automaton model as the feature selector. The methodology developed here is intended to be deployed in conjunction with any one of the numerous recursive schemes of clustering in which the crudely formed initial clusters are refined in a recursive fashion by successively determining the centroids of the different clusters and reallocating the samples to the clusters defined by these centroids. This allocation is carried out on the basis of distance measures (Euclidean or modifications thereof) and is in parallel progress with the feature-evaluation process. The clusters, as formulated at each stage of the recursive process, provide the pseudosupervised environment for the feature selector. The track record of the automaton in terms of probabilities of penalized action provides a measure of the efficiency of the different feature subsets in the unsupervised environment.