Ensemble-based classifiers for prostate cancer diagnosis

Abstract

In this paper, we address microarray data sets dimensionality problem to achieve early and accurate diagnosis of prostate cancer without need to biopsy operation based rotation multiple classifier forest system. To evaluate the performance of presented approach, we present tests on different prostate data sets. The experimental results obtained, show that the overall accuracy offered by the employed technique is high compared with other machine learning techniques including random forest classifier, single decision trees and rough sets as well as features were reduced from 12600 features to 89 features using correlation filter method.