Title :
Estimating neuronal variable importance with Random Forest
Author :
Oh, Jong ; Laubach, Mark ; Luczak, Artur
Author_Institution :
John B. Pierce Lab., Yale Univ., New Haven, CT, USA
Abstract :
We describe a novel application of a new method for data mining, Random Forest, for the analysis of data sets acquired with neuronal ensemble recording methods. Random Forest is used here to measure the relative importance of each input variable in the data. The technique is fast and can greatly reduce the number of variables with little compromise, especially for highly redundant data like neural ensemble spike trains. It also naturally preserves the identifiability of the original Information source unlike other techniques, for example the principal component analysis that mixes up the content of the variables irrecoverably and projects it into different set of variables.
Keywords :
bioelectric potentials; brain; data mining; principal component analysis; Random Forest; data mining; data sets; highly redundant data like neural ensemble spike trains; input variable; neuronal ensemble recording methods; neuronal variable importance; principal component analysis; Bagging; Data analysis; Error analysis; Input variables; Laboratories; Machine learning algorithms; Neurons; Radio frequency; Sampling methods; Signal processing algorithms;
Conference_Titel :
Bioengineering Conference, 2003 IEEE 29th Annual, Proceedings of
Print_ISBN :
0-7803-7767-2
DOI :
10.1109/NEBC.2003.1215978