A Deep Stacked Ensemble Model for Microarray Data Classification with Boosted Meta Classifier

Abstract

Classification of microarray data is one of the major research interests in the biomedical field. It allows physicians for early detection of cancer through analysis of the Deoxyribonucleic acid (DNA). Classification of these sensitive data is still challenging due to the small sample and more feature size. In this paper, the authors have used an ensemble model for classifying two types of leukemia as Acute lymphocytic leukemia (ALL) and Acute myelocytic leukemia (AML). The work is carried out with other types of genetic data such as Leukemia, lung tumor, liver cancer, and liver Cirrhosis. The biomedical data are imbalanced. The ensemble classifier is based on a stacked approach where deep neural network (DNN) classifiers are used as the base classifier. The structure of each DNN is chosen as the homogenous type for the same training process for all classifiers. Because of the adaptive nature and random weight initialization, it provides different results for each classifier. The outputs of the base classifiers are again fed to a gradient boosting ensemble model termed a meta classifier. The meta classifier provides the final classification output. For comparison purposes, two types of meta-classifiers such as support vector machine (SVM) and ensemble gradient boosting are used in the proposed work. The performance of the model is verified well and the results are provided in the result section. From the experimental result, it is observed that the classification accuracy is 96% with SVM and 98% with boosted meta classifier for leukemia data, whereas 99.04% for lung tumor and 99.03% for liver Cirrhosis.