A Robust Ensemble Classification Method for Microarray Data Analysis

Apart from the dimensionality problem, the uncertainty of Microarray data quality is another major challenge of Microarray classification. Microarray data contains various levels of noise and quite often are high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. In this paper, we propose a new Microarray data classification method, based on diversified multiple trees. The new method contains features that, (1) make most use of the information from the abundant genes in the Microarray data, and (2) use a unique diversity measurement in the ensemble decision committee. The experimental results show that the proposed classification method (DMDT) and the well known method (CS4), which diversifies trees by using distinct tree roots, are more accurate on average than other well-known ensemble methods, including Bagging, Boosting and Random Forests. The experiments also indicate that using diversity measurement of DMDT improves the classification accuracy of ensemble classification on Microarray data.