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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics
verfasst von : Jan Struyf, Sašo Džeroski, Hendrik Blockeel, Amanda Clare
Erschienen in: Progress in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.