Performance Analysis Of Genetic Algorithm With Knn And Svm For Feature Selection In Tumor Classification

Concepts

ComputingOncologyData MiningFeature EngineeringMachine LearningClassificationFeature SelectionGenetic AlgorithmsKernel MethodsSupport Vector MachinesTumor ClassificationComputer-aided Tumor DiagnosisEvolutionary Feature SelectionFeature Selection For ClassificationGenetic Algorithm OptimizationGenetic Algorithm Variable SelectionUnsupervised Feature Selection

Abstract

Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.