Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics
2005
Struyf, Jan | Džeroski, Sašo | Blockeel, Hendrik | Clare, Amanda
This paper investigates how predictive clustering trees canbe used to predict gene function in the genome of the yeast Saccharomycescerevisiae. We consider the MIPS FunCat classification scheme,in which each gene is annotated with one or more classes selected froma given functional class hierarchy. This setting presents two importantchallenges to machine learning: (1) each instance is labeled with a set ofclasses instead of just one class, and (2) the classes are structured in a hierarchy;ideally the learning algorithm should also take this hierarchicalinformation into account. Predictive clustering trees generalize decisiontrees and can be applied to a wide range of prediction tasks by pluggingin a suitable distance metric. We define an appropriate distance metricfor hierarchical multi-classification and present experiments evaluatingthis approach on a number of data sets that are available for yeast.
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