Evaluation Method in Random Forest as Applied to Microarray Data

Ng, Ee Ling and Abu Hasan, Yahya (2008) Evaluation Method in Random Forest as Applied to Microarray Data. Malaysian Journal of Mathematical Sciences, 2 (2). pp. 73-81. ISSN 1823-8343

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Abstract

Unlike other decision tree classifiers, Random Forest grows multiple trees which create a forest-like classification. Thus, Random Forest produces better performance as compared to that of a single tree classifier. We consider several evaluation methods which include the 10-fold cross validation, leave-one-out cross validation and bootstrap estimation. These evaluation methods are to assess the performance of the Random Forest classifier. The usage of different evaluation methods certainly shows the durability of Random Forest. To help illustrate the problem better, the four microarray datasets of binary-class and multi-class are used as experimental datasets. The evaluation method is a subjective issue and it is bound to the researcher and his study scope when selecting an evaluation method. However, we have shown that Random Forest is best evaluated using 10-fold cross validation and bootstrap estimation.

Item Type:Article
Keyword:Microarray, Random Forest, Classification, Evaluation Methods
Faculty or Institute:Institute for Mathematical Research
Publisher:UPM Press
ID Code:12600
Deposited By: Najwani Amir Sariffudin
Deposited On:09 Jun 2011 09:35
Last Modified:27 May 2013 07:52

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