Separation and recovery Markov boundary discovery and its application in EEG-based emotion recognition

• DOI: 10.1016/j.ins.2021.04.071
• Publication Date: 2021-04-24T03:45:17Z
• Authors: Xingyu Wu; Bingbing Jiang; Kui Yu; Huanhuan Chen
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract In a Bayesian network (BN), the Markov boundary (MB) presents the local causal structure around a target. Due to the interpretability and robustness, it has been widely applied to feature selection and BN structure learning. However, existing MB discovery algorithms might fail to identify some true positives, leading to poor performance in real-world applications. To tackle this issue, we introduce a two-phase-discovery strategy to search more true positives. Based on this strategy, we propose a more accurate and data-efficient algorithm, separation and recovery MB discovery algorithm (SRMB). SRMB first discovers an incomplete parent–child set and spouse set via an MB separation process, and then retrieves the ignored true positives via an MB recovery process, which further exploits a symmetry test to improve accuracy in unfaithful cases. Experiments on standard BN and real-world data sets demonstrate the effectiveness and superiority of SRMB in terms of MB discovery, BN structure learning, and feature selection. To demonstrate the superiority of SRMB in data with distribution shift, we further apply SRMB to EEG-based emotion recognition tasks, where distribution shift exists in multiple unstable sessions. We prove that the most predictive features are from Gamma/Beta frequency bands and are distributed at the lateral temporal area.