Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data

The high dimensionality of data is a common problem in classification. In this work, a small number of significant features is investigated to classify data of two sample groups. Various feature selection and classification techniques are applied in a collection of four high-throughput DNA methylation microarray data sets. Using accuracy as a performance metric, the repeated 10-fold cross-validation strategy is implemented to evaluate the different proposed techniques. Combining the Signal to Noise Ratio (SNR) and Wilcoxon rank-sum test filter methods with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) as an embedded method has resulted in a perfect performance. In addition, the linear classifiers showed excellent results compared to others classifiers when applied to such data sets.