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Machine learning-based feature ranking: Statistical interpretation and gene network inference.

. University of Liège, Belgium, (2012)

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Knowledge-Guided Additive Modeling for Supervised Regression., , and . DS, volume 14276 of Lecture Notes in Computer Science, page 64-78. Springer, (2023)Exploiting tree-based variable importances to selectively identify relevant variables., , and . FSDM, volume 4 of JMLR Proceedings, page 60-73. JMLR.org, (2008)Nets Versus Trees for Feature Ranking and Gene Network Inference., , , , and . DS, volume 12323 of Lecture Notes in Computer Science, page 231-245. Springer, (2020)Optimizing model-agnostic random subspace ensembles., and . Mach. Learn., 113 (2): 993-1042 (February 2024)Statistical interpretation of machine learning-based feature importance scores for biomarker discovery., , , and . Bioinform., 28 (13): 1766-1774 (2012)Combining tree-based and dynamical systems for the inference of gene regulatory networks., and . Bioinform., 31 (10): 1614-1622 (2015)Optimizing model-agnostic Random Subspace ensembles., and . CoRR, (2021)Context-dependent feature analysis with random forests., , , , and . UAI, AUAI Press, (2016)Machine learning-based feature ranking: Statistical interpretation and gene network inference.. University of Liège, Belgium, (2012)From global to local MDI variable importances for random forests and when they are Shapley values., , , , and . NeurIPS, page 3533-3543. (2021)