Exclusivity Regularized Machine: A New Ensemble SVM Classifier

Xiaojie Guo, Xiaobo Wang, Haibin Ling
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
The diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble SVM classifier, namely Exclusivity Regularized Machine (ExRM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution
more » ... ExRM. Theoretical analysis on convergence, global optimality and linear complexity of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-arts in terms of accuracy and efficiency.
doi:10.24963/ijcai.2017/241 dblp:conf/ijcai/GuoWL17 fatcat:sstxcdvfbrbzzmtq24fppsi3bq