A diverse meta learning ensemble technique to handle imbalanced microarray dataset

Abstract

One of the challenging issues in bioinformatics field is that, microarray datasets are imbalance in nature i.e., the majority class dominates the minority class making it difficult for the conventional classifiers to achieve accurate and useful predictions. However, some studies have addressed this issue merely by focusing on binary-class problems. In this article, an ensemble framework is proposed for multiclass imbalance classification problem that combines a meta learning algorithm ‘decorate’ with a sampling technique to deal with the problem in microarray datasets. The meta-learning algorithm builds diverse ensembles of classifiers constructing artificial samples and the sampling technique introduces bias to achieve uniform class distribution to reduce misclassification error. Experimental results on the two highly imbalanced multiclass microarray cancer datasets indicate that the technique applied provides significant improvement in comparison to other conventional ensembles.