Local Modelling in Classification

Authors:
Gero Szepannek;Julia Schiffner;Julie Wilson;Claus Weihs
Affiliations:
Department of Statistics, University of Dortmund, Dortmund, Germany 44227;Department of Statistics, University of Dortmund, Dortmund, Germany 44227;Department of Mathematics and Chemistry, University of York Heslington, York, UK YO1 5DD;Department of Statistics, University of Dortmund, Dortmund, Germany 44227
Venue:
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Year:
2008

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Abstract

In classification tasks it may sometimes not be meaningful to build single rules on the whole data. This may especially be the case if the classes are composed of several subclasses. Several common as well as recent issues are presented to solve this problem. As it can e.g. be seen in Weihs et al. (2006) there may result strong benefit from such local modelling. All presented methods are evaluated and compared on four real-world classification problems in order to obtain some overall ranking of their performance following an idea of Hornik and Meyer (2007).