Subscription or Fee Access
k-SS: a sequential feature selection and prediction method in Microarray study
Selection of the optimal feature subsets from high dimensional genomic data for predicting tumour conditions of tissue samples is the prime focus in many recent microarray studies. In this paper, we present a modified sequential features selection and classification procedure, the k-SS method, which ensures proper selection of relevant gene chips with correlated expression profiles to the existing cancer sub-groups in any binary-response microarray classification problem. The k-SS algorithm uses the misclassification error rate as its feature selection criteria. The procedure avoids being trapped in a local optimal feature selection step by allowing the level of significance level (of the k-SS features selection tests) at which the best combination of features is selected to be freely determined by cross-validation. This new method competes favourably with eight of the existing machine learning methods considered in this study. In many instances, the k-SS classification method performs excellently well in terms of better prediction accuracy relative to others. Nine published microarray data sets are used to demonstrate our results.
k-SS method, Misclassification error rate, Skew-Normal density, Microarray data
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.