Ensemble-based classifiers for cancer classification using human tumor microarray data

In this paper, two cancer classification techniques based on multicategory microarray data sets are presented. Due to the high dimensionality of microarray data sets, choosing reliable feature selection and classification algorithms with a high degree of accuracy and a low complexity is a crucial task in bioinformatics. Toward this goal, this paper aims to maximize the cancer classification accuracy using two reliable ensemble-based classifiers namely the ensemble of naive bayes and the ensemble of k-nearest neighbor. Simulation results show that our classifiers have considerably better accuracy than some conventional classification techniques such as the Support Vector Machine (SVM) and artificial neural networks in the field of multicategory microarray cancer classification based on fourteen cancer data set. However, the run time of the introduced ensemble-based classifiers is longer when the schemes use whole features. To reduce the time complexity while preserving the same classification accuracy as before, we use the recursive feature elimination based on the multiple support vector machine classifier to select more informative genes before applying the ensemble-based classifiers. Numerical evaluations show at least 30% improvement in the classification accuracy of our schemes when compared to the SVM-one versus one rule. In addition, our schemes are much more robust to the feature elimination and display a high accuracy in the case of low number of features.