Independence regularized multi-label ensemble

Ziwei Yu, Changqing Zhang, Qinghua Hu, Pengfei Zhu

Abstract: In this paper, we focus on promoting multi-label learning task with ensemble learning. Compared to traditional single algorithm methods, it has been recognized that ensemble methods could achieve much better performance than each constituent learned model, especially under the conditional independence of different classifiers. Existing multi-label ensemble algorithms mainly focus on creating diverse component learners by employing different mechanisms, mostly using randomization strategies by smart heuristics. Different from most existing methods, in this paper, we propose an ensemble method to learn the basic classifiers which considers the general independence of the different classifiers. Therefore, each learned multi-label classifier is guaranteed to be diverse and complementary. Furthermore, considering the different qualities of these classifiers, a weight vector is learned to balance these classifiers. Experiments on several benchmark datasets well demonstrate that the proposed method outperforms the state-of-the-art methods.