Local Feature Selection For Heterogeneous
Problems
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
Related Book
Other papers in this volume
Warning (2)
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Code
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