Towards scalable and data efficient learning of Markov boundaries

DOI10.1016/j.ijar.2006.06.008zbMath1122.68136OpenAlexW2013374740MaRDI QIDQ997045

Roland Nilsson, Jesper Tegnér, Johan Björkegren, José-Maria Peña

Publication date: 19 July 2007

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2006.06.008

zbMATH Keywords

classificationBayesian networksfeature subset selection

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (18)

Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach ⋮ Efficient score-based Markov blanket discovery ⋮ Towards fast and efficient algorithm for learning Bayesian network ⋮ Joint Markov blankets in feature sets extracted from wavelet packet decompositions ⋮ Swamping and masking in Markov boundary discovery ⋮ Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood ⋮ Unnamed Item ⋮ Structural learning for Bayesian networks by testing complete separators in prime blocks ⋮ Score-based methods for learning Markov boundaries by searching in constrained spaces ⋮ Domain knowledge-enhanced variable selection for biomedical data analysis ⋮ A novel feature selection using Markov blanket representative set and particle swarm optimization algorithm ⋮ A greedy feature selection algorithm for big data of high dimensionality ⋮ Learning Bayesian networks based on bi-velocity discrete particle swarm optimization with mutation operator ⋮ The difficulty of being moral ⋮ Learning Gaussian graphical models with fractional marginal pseudo-likelihood ⋮ Unnamed Item ⋮ Feature selection for Bayesian network classifiers using the MDL-FS score ⋮ Extending greedy feature selection algorithms to multiple solutions

Uses Software

• TETRAD
• MLC++

Cites Work

• Unnamed Item
• Unnamed Item
• The max-min hill-climbing Bayesian network structure learning algorithm
• Causation, prediction, and search
• Statistical challenges in functional genomics. (With comments and a rejoinder).
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty

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