AdaBoost Parallelization on PC Clusters with Virtual Shared Memory for Fast Feature Selection

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceFeature selection is a key issue in many machine learning applications and the need to test lots of candidate features is real while computational time required to do so is often huge. In this paper, we introduce a parallel version of the well- known AdaBoost algorithm to speed up and size up feature selection for binary classification tasks using large training datasets and a wide range of elementary features. This parallelization is done without any modification to the AdaBoost algorithm and designed for PC clusters using Java and the JavaSpace distributed framework. JavaSpace is a memory sharing paradigm implemented on top of a virtual shared memory, that appears both efficient and easy-to-use. Results and performances on a face detection system trained with the proposed parallel AdaBoost are presented