Using stacking ensemble for microarray-based cancer classification

Microarray technology has produced a massive amount of gene expression data. This data can be used efficiently for classification that facilitates disease diagnosis and prognosis. There are many computational methods that are utilized for cancer classification using these gene expression data. Artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) are among the most successful methods for classifying tumors. Recent research shows that combining many classifiers can yield better results than using one classifier. In this paper, we used stacking ensemble to combine different classifiers, namely, ANN, SVM, RF, naive Bayes (NB), and knearest neighbors (KNN) for microarray-based cancer classification. Results show that stacking ensemble performed better in terms of accuracy, kappa coefficient, sensitivity, specificity, area under the curve (AUC), and receiver operating characteristic (ROC) curve, when applied to publicly available microarray data.