Malaysian Journal of Mathematical Sciences, July 2008, Vol. 2(S)
Special Edition - National Conference on Mathematical Biology 2006
Evaluation Method in Random Forest as Applied to Microarray Data
Ng Ee Ling and Yahya Abu Hasan
Corresponding Email: [email protected]
Received date: -
Accepted date: -
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.
Keywords: Microarray; Random Forest; Classification; Evaluation Methods
