Ensemble multi-label learning based on neural network
Hu Li, Peng Zou, Weihong Han, Rongze Xia, Fei Liu · 2013
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.