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Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an empirical study of their use in functional genomics. We compare(More)
In this article, we address the task of learning models for predicting structured outputs. We consider both global and local prediction of structured outputs, the former based on a single model that predicts the entire output structure and the latter based on a collection of models, each predicting a component of the output structure. We use ensemble(More)
S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains(More)
In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Relational classifiers differ with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals of the set,(More)
The term “model trees” is commonly used for regression trees that contain some non-trivial model in their leaves. Popular implementations of model tree learners build trees with linear regression models in their leaves. They use reduction of variance as a heuristic for selecting tests during the tree construction process. In this article, we show that(More)
We propose a simple yet effective strategy to induce a task dependent feature representation using ensembles of random decision trees. The new feature mapping is efficient in space and time, and provides a metric transformation that is non parametric and not implicit in nature (i.e. not expressed via a kernel matrix), nor limited to the transductive setup.(More)