Multi-label Feature Selection via Information Gain

Multi-label classification has gained extensive attention recently. Compared with traditional classification, multi-label classification allows one instance to associate with multiple labels. The curse of dimensionality existing in multi-label data presents a challenge to the performance of multi-label classifiers. Multi-label feature selection is a powerful tool for high-dimension problem. However, the existing feature selection methods are unable to take both computational complexity and label correlation into consideration. To address this problem, a new approach based on information gain for multi-label feather selection (IGMF) is presented in this paper. In the process of IGMF, Information gain between a feature and label set is exploited to measure the importance of the feature and label corrections. After that, the optimal feature subset are obtained by setting the threshold value. A series of experimental results show that IGMF can promote performance of multi-label classifiers.