Inference of genetic networks using random forests:Performance improvement using a new variable importance measure
Author:
Affiliation:
1. Faculty of Engineering, Tottori University
2. Graduate School of Sustainability Sciences, Tottori University
3. Institute for Protein Research, Osaka University
Publisher
Chem-Bio Informatics Society
Subject
Biochemistry
Link
https://www.jstage.jst.go.jp/article/cbij/22/0/22_88/_pdf
Reference42 articles.
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3. [3] Archer, K. J.; Kimes, R. V. Empirical characterization of random forest variable importance measures, Comput. Stat. Data Anal., 2008, 2249–2260. doi: 10.1016/j.csda.2007.08.015
4. [4] Breiman, L. Random forests, Machine Learning, 2001, 45, 5–32. doi: 10.1023/A:1010933404324
5. [5] Cai, J.; Kuo, J.; Wang, S; Yang, S. Feature selection in machine learning: A new perspective, Neurocomputing, 2018, 300, 70–79. doi: 10.1016/j.neucom.2017.11.077
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