Ensemble of Kernel Based Classifiers to Improve the Human Cancer Prediction using DNA Microarrays

DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of samples. Support Vector Machines (SVM), have been applied to the classification of cancer samples with encouraging results. However, they are usually based on Euclidean distances that fail to reflect accurately the sample proximities. Besides, SVM classifiers based on non-Euclidean dissimilarities fail to reduce significantly the errors. In this paper, we propose an ensemble of SVM classifiers in order to reduce the misclassification errors. The diversity among classifiers is induced considering a set of complementary dissimilarities and kernels. The experimental results suggest that that our algorithm improves classifiers based on a single dissimilarity and a combination strategy such as Bagging.