Hierarchical multi-classification with predictive clustering trees in functional genomics

Authors:
Jan Struyf;Sašo Džeroski;Hendrik Blockeel;Amanda Clare
Affiliations:
Dept. of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium;Dept. of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia;Dept. of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium;Dept. of Computer Science, The University of Wales, Aberystwyth, Penglais, Aberystwyth, Ceredigion, Wales, UK
Venue:
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Year:
2005

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Abstract

This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast Saccharomyces cerevisiae. We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hierarchy. This setting presents two important challenges to machine learning: (1) each instance is labeled with a set of classes instead of just one class, and (2) the classes are structured in a hierarchy; ideally the learning algorithm should also take this hierarchical information into account. Predictive clustering trees generalize decision trees and can be applied to a wide range of prediction tasks by plugging in a suitable distance metric. We define an appropriate distance metric for hierarchical multi-classification and present experiments evaluating this approach on a number of data sets that are available for yeast.