Estimating neuronal variable importance with random forest

Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC

Volumn , Issue , 2003, Pages 33-34

  Oh, Jong ^a,d   Laubach, Mark ^a,c   Luczak, Artur ^a,b  

^a YALE UNIVERSITY   (United States)

^b Yale University   (United States)

^c YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d VANDERBILT UNIVERSITY   (United States)

Author keywords

Bagging; Brain Machine Interface; Decision tree; Random forest; Randomization; Variable importance

Indexed keywords

BRAIN; DECISION THEORY; HUMAN COMPUTER INTERACTION; INTERFACES (COMPUTER); NEUROLOGY; PRINCIPAL COMPONENT ANALYSIS; RANDOM PROCESSES; TREES (MATHEMATICS); ARTIFICIAL INTELLIGENCE; DATA MINING; DATA REDUCTION; DECISION TREES; ERROR ANALYSIS; INFORMATION ANALYSIS; LABORATORIES; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURONS; SIGNAL PROCESSING;

RANDOMIZATION METHODS; BAGGING; INPUT VARIABLES; RADIO FREQUENCIES; SAMPLING METHOD; SIGNAL PROCESSING ALGORITHMS;

BIOMEDICAL ENGINEERING; PRINCIPAL COMPONENT ANALYSIS;

EID: 38149107937     PISSN: 1071121X     EISSN: 21607001     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. L. Breiman, "Random Forest" University of California Berkeley, Statistics Department Technical Report, January 2001.
2. L. Breiman, "Classification and Regression Trees," Belmont, CA: Wadsworth International Group, 1984.
3. L. Breiman, "Bagging Predictors," University of California Berkeley, Statistics Department Technical Report No. 421, September 1994.
4. M. Laubach, J. Wessberg, and M.A. Nicolelis, "Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task," Nature, Vol. 405, pp. 567-571, Jun 2000.