NDLI: Using random forests to uncover bivariate interactions in high dimensional small data sets

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Using random forests to uncover bivariate interactions in high dimensional small data sets

Content Provider	ACM Digital Library
Author	Navarro, Hilario Arevalillo, Jorge M.
Abstract	Random Forests (RF) is an ensemble method which has become widely accepted within the machine learning and bioinformatics communities in the last few years. Its predictive strength, along with some of the ingredients --- rich in information --- provided by the output, has made RF an efficient Data Mining tool for discovering patterns in data. In this paper we review the learning mechanism of RF within the classification setting and apply it to uncover bivariate interactions, carrying on useful information about an outcome, in high dimensional low sample data. We propose a divide and conquer search strategy in the variable space that benefits from the ranking of variable importances of RF at a first stage, along with the out of bag error rate (oob) of the ensemble at a second stage. The procedure combines both elements in order to capture difficult to uncover patterns in these type of data. We will show the performance of our procedure in some synthetic scenarios and will give a real application to a microarray data set in order to illustrate how it works.
Starting Page	3
Ending Page	6
Page Count	4
File Format	PDF
ISBN	9781605586670
DOI	10.1145/1562090.1562091
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Microarray data Bivariate interactions Random forests
Content Type	Text
Resource Type	Article

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