FSVML AND GA-FSVML WRAPPER APPROACHES FOR GENE SELECTION AND CLASSIFICATION USING EXPRESSIONS OF VERY FEW GENES

Recently, Gene expression profiling by microarray technique has been effectively utilized for classification and diagnostic guessing of cancer nodules in the field of medical sciences. But the techniques used for cancer classification is still in its lower level. There are various drawbacks in the existing classification techniques such as low testing accuracy, high training time, unreliability, etc. Moreover, microarray data consists of a high degree of noise. Gene ranking techniques such as T-Score, ANOVA, etc are later proposed to overcome those problems. But those approaches will sometimes wrongly predict the rank when large database is used. To overcome these issues, this paper mainly focuses on the development of an effective feature selection and classification technique for microarray gene expression cancer diagnosis for provide significant accuracy, reliability and less error rate. In this paper, Wrapper feature selection approach called the GA-FSVML approach is used for the effective feature selection of genes. In FSVML, the RBF kernel function in SVM is trained using modified Levenberg Marquadt algorithm. This approach proposes a Fast SVM Learning (FSVML) technique for the classification tasks. The experiment is performed on lymphoma data set and the result shows the better accuracy of the proposed FSVML with GA-FSVML classification approach when compared to the standard existing approaches.