2011 MiningDatawithRandomForestsASur

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Subject Headings: Random Forests Algorithm.


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Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2011 MiningDatawithRandomForestsASurAntanas Verikas
Adas Gelzinis
Marija Bacauskiene
Mining Data with Random Forests: A Survey and Results of New Tests10.1016/j.patcog.2010.08.0112011
AuthorAntanas Verikas +, Adas Gelzinis + and Marija Bacauskiene +
doi10.1016/j.patcog.2010.08.011 +
titleMining Data with Random Forests: A Survey and Results of New Tests +
year2011 +