Open Access

ARTICLE

Online Markov Blanket Learning with Group Structure

Bo Li¹, Zhaolong Ling¹, Yiwen Zhang^1,*, Yong Zhou¹, Yimin Hu², Haifeng Ling³

1 School of Computer Science and Technology, Anhui University, Hefei, Anhui Province, 230601, China
2 Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui Province, 230031, China
3 School of Management, Hefei University of Technology, Hefei, Anhui Province, 230009, China

* Corresponding Author: Yiwen Zhang. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 33-48. https://doi.org/10.32604/iasc.2023.037267

Received 28 October 2022; Accepted 16 December 2022; Issue published 29 April 2023

Abstract

Learning the Markov blanket (MB) of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables. Online MB Learning can learn MB for a given variable on the fly. However, in some application scenarios, such as image analysis and spam filtering, features may arrive by groups. Existing online MB learning algorithms evaluate features individually, ignoring group structure. Motivated by this, we formulate the group MB learning with streaming features problem, and propose an Online MB learning with Group Structure algorithm, OMBGS, to identify the MB of a class variable within any feature group and under current feature space on the fly. Extensive experiments on benchmark Bayesian network datasets demonstrate that the proposed algorithm outperforms the state-of-the-art standard and online MB learning algorithms.

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Keywords

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