A New Framework for Multiclass Classification Using Multiview Assisted Adaptive Boosting

Abstract

Multiview representation of data is common in disciplines such as computer vision, bio-informatics, etc. Traditional fusion methods train independent classifiers on each view and finally conglomerate them using weighted summation. Such approaches are void from inter-view communications and thus do not guarantee to yield the best possible ensemble classifier on the given sample-view space. This paper proposes a new algorithm for multiclass classification using multi-view assisted supervised learning (MA-AdaBoost). MA-AdaBoost uses adaptive boosting for initially training baseline classifiers on each view. After each boosting round, the classifiers share their classification performances. Based on this communication, weight of an example is ascertained by its classification difficulties across all views. Two versions of MA-AdaBoost are proposed based on the nature of final output of baseline classifiers. Finally, decisions of baseline classifiers are agglomerated based on a novel algorithm of reward assignment. The paper then presents classification comparisons on benchmark UCI datasets and eye samples collected from FERET database. Kappa-error diversity diagrams are also studied. In majority instances, MA-AdaBoost outperforms traditional AdaBoost, variants of AdaBoost, and recent works on supervised collaborative learning with respect to convergence rate of training set and generalization errors. The error-diversity results are also encouraging.