PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Boosting Classifiers Built from Different Subsets of Features
Jean-Christophe Janodet, Marc Sebban and Henri-Maxime Suchier
Fundamenta Informaticae Volume 96, Number 1-2, pp. 89-109, 2009. ISSN 0169-2968 (Print) 1875-8681 (Online)


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 ourmethod works significantly better than any combination of independent boosting procedures.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5640
Deposited By:Marc Sebban
Deposited On:08 March 2010