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We empirically evaluate several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. We then propose a new method for stacking, that uses multi-response model trees at the meta-level, and show that(More)
Meta decision trees (MDTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MDTs into the Weka data mining suite. We compare classifier ensembles combined with MDTs to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting and stacking with three(More)
The two most commonly addressed data mining tasks are predictive modelling and clustering. Here we address the task of predic-tive clustering, which contains elements of both and generalizes them to some extent. We propose a novel approach to predictive clustering called predictive clustering rules, present an initial implementation and its preliminary(More)
In this paper we investigate the problem of evaluating ranked lists of biomarkers, which are typically an output of the analysis of high-throughput data. This can be a list of probes from microarray experiments, which are ordered by the strength of their correlation to a disease. Usually, the ordering of the biomarkers in the ranked lists varies a lot if(More)