文件名称：Multi-Label Ensemble Learning
文件大小：365KB
文件格式：PDF
更新时间：2019-03-21 17:06:56
Multi-Label Ensemble Learning
Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the gener- alization ability of such individual learner can be weak. It is well known that ensemble learning can effectively improve the generalization abil- ity of learning systems by constructing multiple base learners and the performance of an ensemble is related to the both accuracy and diver- sity of base learners. In this paper, we study the problem of multi-label ensemble learning. Specifically, we aim at improving the generalization ability of multi-label learning systems by constructing a group of multi- label base learners which are both accurate and diverse. We propose a novel solution, called EnML, to effectively augment the accuracy as well as the diversity of multi-label base learners. In detail, we design two objective functions to evaluate the accuracy and diversity of multi- label base learners, respectively, and EnML simultaneously optimizes these two objectives with an evolutionary multi-objective optimization method. Experiments on real-world multi-label learning tasks validate the effectiveness of our approach against other well-established methods.