Stacked regression ensemble for cancer class prediction

Design of a machine learning algorithm as a robust class predictor for various DNA microarray datasets is a challenging task, as the number of samples are very small as compared to the thousands of genes (feature set). For such datasets, a class prediction model could be very successful in classifying one type of dataset but may fail to perform in a similar fashion for other datasets. This paper presents a stacked regression ensemble (SRE) model for cancer class prediction. Results indicate that SRE has provided performance stability for various microarray datasets. The performance of SRE has been cross validated using the k-fold cross validation method (leave one out) technique for BRCA1, BRCA2 and sporadic classes for ovarian and breast cancer microarray datasets. The paper also presents comparative results of SRE with most commonly used SVM and GRNN. Empirical results confirmed that SRE has demonstrated better performance stability as compared to SVM and GRNN for the classification of assorted cancer data.