Dimensionality Reduction for Feature and Pattern Selection in Classification Problems

Reducing the dimensionality of a dataset is an important and often challenging task. This can be done by either reducing the number of features, a task called feature selection, or by reducing the number of patterns, called data reduction. In this paper we propose methods that employ a novel concept called Discernibility for achieving these two tasks separately, with the aim to solve classification problems. The experimental results verify our claim that the proposed methods are a viable alternative for dimensionality reduction, for various datasets and a variety of classifiers.