Online Markov Blanket Learning for High-Dimensional Data

作者：Ling, Zhaolong; Li, Bo; Zhang, Yiwen^*; Li, Ying; Ling, Haifeng

来源：Applied Intelligence, 2023, 53(5): 5977-5997.

DOI：10.1007/s10489-022-03841-5

• Markov blanket
• Bayesian network
• Streaming features

Since the Markov blanket (MB) of a class variable captures the causal relationship between the class variable and selected features, employing the MB of a class variable for feature selection improves the interpretability and robustness of the predictive model. Online MB learning aims to identify the MB with streaming features. However, the only existing online MB learning algorithm needs to enumerate the subsets of selected PC (i.e., parents and children) and spouses and may include false-positives in the found MB, thus affecting the efficiency and accuracy on high-dimensional data. To address this issue, in this paper, we propose two online MB learning algorithms, called Online SimulTaneous MB learning (O-ST) algorithm and Online Divide-and-Conquer MB learning (O-DC) algorithm. When a new feature arrived, O-ST simultaneously learns the PC and spouses (i.e., the MB) conditioned on the currently selected MB, and O-DC learns the PC and spouses separately by sequentially comparing the mutual information in the currently selected PC. The comprehensive experimental results validate that the proposed algorithms achieve higher efficiency and better accuracy than the state-of-the-art online MB learning algorithms.