Multiclass Sequential Feature Selection and Classification Method for Genomic Data

This paper presents an efficient multiclass sequential feature selection and classification (mk-SS) method using gene expression signatures. The development of this method employs 10-fold cross-validation to ensure stability. The efficiency of this method is assessed through the misclassification error rate and some other performance measures. The performances of the mk-SS were compared with the classification results of the Support Vector Machines (SVM) over five published multiclass microarray datasets. The results showed that the mk-SS method efficiently selects the informative gene biomarkers for proper classification of the biological groups of the tissue samples. This method competes favourably with SVM in terms of prediction accuracy while it outperforms the SVM in 80% of cases considered. The quality of the features selected by mk-SS algorithm was validated by hybridizing the feature selection scheme of the mk-SS into the standard SVM algorithm which significantly improves the predictive power of the standard SVM method. This work has shown that classification of various cancer type using gene expression profiles is feasible especially when the endpoints are of multi-category. Keywords: k-SS, mk-SS, Support Vector Machines, Microarray, Misclassification error rate

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