Results 251 to 260 of about 698,809 (305)

A novel feature selection using Markov blanket representative set and Particle Swarm Optimization algorithm

Computational and Applied Mathematics, 2023
Youlong Yang, Liqin Sun
exaly   +2 more sources

Improving the performance and explainability of knowledge tracing via Markov blanket

Information Processing and Management
Bo Jiang, Yuang Wei
exaly   +2 more sources

On Markov blankets and hierarchical self-organisation

Journal of Theoretical Biology, 2020
Biological self-organisation can be regarded as a process of spontaneous pattern formation; namely, the emergence of structures that distinguish themselves from their environment. This process can occur at nested spatial scales: from the microscopic (e.g., the emergence of cells) to the macroscopic (e.g. the emergence of organisms).
Thomas Parr, Karl J. Friston, Ensor Rafael Palacios   +2 more
exaly   +7 more sources

Error-aware Markov blanket learning for causal feature selection

Information Sciences, 2022
Causal feature selection has attracted much attention in recent years, since it has better robustness than the traditional feature selection. Existing causal feature selection algorithms aim to identify a Markov blanket (MB) of the class variable. The MB
Xianjie Guo, Kui Yu, Fu-Yuan Cao, Peipei Li, Hao Wang   +4 more
semanticscholar   +1 more source

LAGAM: A Length-Adaptive Genetic Algorithm With Markov Blanket for High-Dimensional Feature Selection in Classification

IEEE Transactions on Cybernetics, 2022
Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability.
Jun-Hai Zhou, Quan-Wang Wu, Meng-Chu Zhou, Jun-Hao Wen, Y. Al-Turki, A. Abusorrah   +5 more
semanticscholar   +1 more source

Efficient score-based Markov Blanket discovery

International Journal of Approximate Reasoning, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qiang Ji, Tian Gao
exaly   +2 more sources

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Tsinghua University

IEEE Transactions on Parallel and Distributed Systems, 2023
Srivastava et al. propose a parallel framework to optimize Bayesian network learning in the SC20 article entitled “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery”.
Juncheng Cao, Kai-Yuan Rong, Ming-Shu Zhai, Ze-Yu Song, Yanyu Ren, Yuxi Zhu, Wen-Tao Han, Ji-Dong Zhai   +7 more
semanticscholar   +1 more source

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Peking University

IEEE Transactions on Parallel and Distributed Systems, 2023
Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely,
Jia-Qi Si, Junyi Guo, Zhewen Hao, Wen He, Ruihan Li, Yueyang Pan, Zhenxin Fu, Chun Fan   +7 more
semanticscholar   +1 more source

Identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry by selecting sample-descriptive components with an Incremental Association Markov Blanket.

Food Research International, 2023
The identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry was attempted in this study. Since soybean paste was odor-rich, 36 components were identified in the imported and domestic soybean samples ...
Seongsoo Jeong, Dokyung Kwon, Jina Lim, Hanbyeol Jang, Jeongkwon Kim, Hoeil Chung   +5 more
semanticscholar   +1 more source

Markov blankets and Bayesian territories

Behavioral and Brain Sciences, 2022
Abstract Bruineberg et al. argue that one ought not confuse the map (model) for the territory (reality) and delineate a distinction between innocuous Pearl blankets and metaphysically laden Friston blankets. I argue that all we have are models, all knowledge is conditional, and that if there is a Pearl/Friston distinction, it is a matter of the ...
openaire   +2 more sources