Kun Liu^1,2, Peiran Li³, Yu Zhang^1,*, Jia Ren¹, Ming Li⁴, Xianyu Wang², Cong Li²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.052166 - 15 August 2024

Abstract When learning the structure of a Bayesian network, the search space expands significantly as the network size and the number of nodes increase, leading to a noticeable decrease in algorithm efficiency. Traditional constraint-based methods typically rely on the results of conditional independence tests. However, excessive reliance on these test results can lead to a series of problems, including increased computational complexity and inaccurate results, especially when dealing with large-scale networks where performance bottlenecks are particularly evident. To overcome these challenges, we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected… More >

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

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 33-48, 2023, DOI:10.32604/iasc.2023.037267 - 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, More >