Boosting Classifiers built from Different Subsets of Features

Jean-Christophe Janodet (1) , Marc Sebban (1) , Henri-Maxime Suchier (1)

1 LHC - Laboratoire Hubert Curien

We focus on the adaptation of boosting to representation spaces composed of different subsets of features. Rather than imposing a single weak learner to handle data that could come from different sources (e.g., images and texts and sounds), we suggest the decomposition of the learning task into several dependent sub-problems of boosting, treated by different weak learners, that will optimally collaborate during the weight update stage. To achieve this task, we introduce a new weighting scheme for which we provide theoretical results. Experiments are carried out and show that our method works significantly better than any combination of independent boosting procedures.

Domains: Machine Learning [cs.LG]

Jean-Christophe Janodet, Marc Sebban, Henri-Maxime Suchier. Boosting Classifiers built from Different Subsets of Features. Fundamenta Informaticae, 2009, 94 (2009), pp.1-21. ⟨10.3233/FI-2009-131⟩. ⟨hal-00403242⟩