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.