Author(s)

I. Skrypnyk, A. Tsymbal & S. Puuronen

Abstract

Current electronic data repositories contain enormous amount of data including also unknown and potentially interesting patterns and relations. One approach commonly used is supervised machine learning, in which a set of training instances is used to train one or more classifiers that map the space formed by different features of the instances into the set of class values. The classifiers are later used to classify new instances with unknown class values. The multidimensional data is sometimes feature-space heterogeneous so that different features have different importance in different subareas of the whole space. In this paper we describe a technique that searches for a division of the feature space identifying the best subsets of features for each instance. Our technique is

Keywords