Feature importance sampling-based adaptive random forest as a useful tool to screen underlying lead compounds

Journal of Chemometrics

Volumn 25, Issue 4, 2011, Pages 201-207

  Cao, Dong Sheng ^a   Liang, Yi Zeng ^a   Xu, Qing Song ^a   Zhang, Liang Xiao ^a   Hu, Qian Nan ^b   Li, Hong Dong ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

ADMET; Feature importance sampling; Machine learning; Random forest; Virtual screening

Indexed keywords

CLASSIFICATION (OF INFORMATION); E-LEARNING; LEAD COMPOUNDS; LEARNING SYSTEMS;

ADMET; BASE CLASSIFIERS; ENSEMBLE APPROACHES; FEATURE IMPORTANCE SAMPLING; MACHINE-LEARNING; PERFORMANCE; RANDOM FORESTS; SAMPLING-BASED; SIMPLE RANDOM SAMPLING; VIRTUAL SCREENING;

IMPORTANCE SAMPLING;

EID: 79954494823     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1375     Document Type: Article

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