Multi-label causal feature selection based on neighbourhood mutual information

Multi-label feature selection has gained significant attention over the past decades. However, most existing algorithms are lack of interpretability and uncover the causal mechanisms. As we know, Markov blanket (MB) is a key concept in Bayesian network, which can be used to represent the local causal structure of a variable and the selected optimal features for multi-label feature selection. To select casual features for multi-label learning, in this paper, Parents and Children (PC) of each label are discovered via the Hiton method. Then, we distinguish P & C and search Spouses (SP) of each label based on neighborhood conditional mutual information. Moreover, the equivalent information phenomenon brought by multi-label datasets will cause some features to be ignored. A metric of conditional independence test is designed, which can be used to retrieve ignored features. In addition, we search common features between relevant labels and label-specific features for a single label. Finally, we propose a Multi-label Causal Feature Selection with Neighbourhood Mutual Information algorithm, called MCFS-NMI. To verify the performance of MCFS-NMI, we compare it with five well-established multi-label feature selection algorithms on six datasets. Experiment results show that the proposed algorithm achieves highly competitive performance against all comparing algorithms.