A comparison on score spaces for expression microarray data classification

In this paper an empirical evaluation of different generative scores for expression microarray data classification is proposed. Score spaces represent a quite recent trend in the machine learning community, taking the best of both generative and discriminative classification paradigms. The scores are extracted from topic models, a class of highly interpretable probabilistic tools whose utility in the microarray classification context has been recently assessed. The experimental evaluation, performed on 3 literature datasets and with 7 score spaces, demonstrates the viability of the proposed scheme and, for the first time, it compares pros and cons of each space

That Elusive Diversity in Classifier Ensembles

Is "useful diversity" a myth? Many experiments and the little available theory on diversity in classifier ensembles are either inconclusive, too heavily assumption-bound or openly non-supportive of the intuition that diverse classifiers fare better than non-divers ones. Although a rough general tendency was confirmed in our previous studies, no prominent link appeared between diversity of the ensemble and its accuracy