Gene-expression-based cancer classification through feature selection with KNN and SVM classifiers

This paper presents a study of feature selection methods effect, using a filter approach, on the accuracy and error of supervised classification of cancer. A comparative evaluation between different selection methods: Fisher, T-Statistics, SNR and ReliefF, is carried out, using the dataset of different cancers; leukemia cancer, prostate cancer and colon cancer. The classification results using k nearest neighbors (KNN) and support vector machine (SVM) classifiers show that the combination between SNR´s method and the SVM classifier can present the highest accuracy.