Ensemble multi-label learning based on neural network

Authors:
Hu Li;Peng Zou;Weihong Han;Rongze Xia;Fei Liu
Affiliations:
National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

Multi-label classification problem refers to predict each instance to be one or more labels in a given label set. It is very common in the real world, e.g. image annotation. Based on a comprehensive analysis of existing researches, we propose a new ensemble learning method for multi-label classification problems. AdaBoost and multi-label neural network are integrated to enhance the generalization ability of the method. Experiments on three standard datasets show that the proposed method performs well.