Integrating label confidence-based feature selection for partial multi-label learning

Qingqi Han, Liang Hu, Wanfu Gao

Abstract: Highlights•A novel feature selection method is proposed for partial multi-label.•A new strategy is proposed for label confidence using two types of sample distances.•Optimization strategy enhances true label identification.•Tests show our method outperforms baselines in partial multi-label tasks.