Classification of Micro Array Gene Expression using k- NN, SVM and Naive Classifiers

Abstract- Classification analysis of microarray gene expression data has been performed widely to find out the biological features and to differentiate intimately related cell types that usually appear in the diagnosis of cancer. Many algorithms and techniques have been developed for the microarray gene classification process. These developed techniques accomplish microarray gene classification process with the aid of three basic phases namely, dimensionality reduction, feature selection and gene classification. In our previous work, microarray gene classification by statistical analysis approach with Fuzzy Inference System (FIS) was proposed for precise classification of genes to their corresponding gene types. This paper proposes an effective classification technique that uses Naïve Bayes classifier, k-NN and SVM. The dimensionality reduction of the gene expression dataset is performed by using statistical approaches. From the dimensionality reduced data, the important genes are identified and also features are extracted. The improved classifier is developed for classification and so it is trained using the classifiers. The well-trained classifier is used to the classification of micro array gene expression dataset