HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection

By Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov. American Medical Informatics Association Annual Symposium, 2003

383 citations recorded by the source corpus.

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Citations in the corpus

Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships (2012, methodology): “The prediction performance using a feed-forward back-propagation neural network was highest for variables selected by HITON-MB, true variable, GS, interIAMBnPC, and PCA while lowest for variables selected by FOU (refer to Table 6).”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “MMMB was tested in the same data sets and compared to prior algorithms such as Koller-Sahami algorithm and IAMB variants with superior results in the quality of Markov blankets.”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, background): “It leverages the estimation of mutual information among variables (Bell and Wang, 2000; Brown et al., 2012; Vergara and Estévez, 2014) or uses classification or regression models to support variable selection (Aliferis et al., 2003, 2010).”
Algorithms for Discovery of Multiple Markov Boundaries (2013, methodology): “Finally, since very fast single Markov boundary induction algorithms have been recently introduced (Aliferis et al., 2010a, 2003a; Peña et al., 2007; Tsamardinos et al., 2003a,b), the overall TIE∗ operation is very fast.”
Model Selection: Beyond the Bayesian/Frequentist Divide (2010, methodology): “Markov blanket methods, for instance, provide powerful feature selection filters (Aliferis et al., 2003).”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “MMHC is released as part of Causal Explorer 1.3 (see Aliferis et al., 2003b for a description of Causal Explorer 1.0), a tool library of local causal discovery and Bayesian network learning algorithms.”