Threshold-based feature selection techniques for high-dimensional bioinformatics data

Analysis conducted for bioinformatics applications often requires the use of feature selection methodologies to handle datasets with very high dimensionality. We propose 11 new threshold-based feature selection techniques and compare the performance of these new techniques to that of six standard filter-based feature selection procedures. Unlike other comparisons of feature selection techniques, we directly compare the feature rankings produced by each technique using Kendall's Tau rank correlation, showing that the newly proposed techniques exhibit substantially different behaviors than the standard filter-based feature selection methods. Our experiments consider 17 different bioinformatics datasets, and the similarities of the feature selection techniques are analyzed using the Frobenius norm. The feature selection techniques are also compared by using Naive Bayes and Support Vector Machine algorithms to learn from the training datasets. The experimental results show that the new procedures perform very well compared to the standard filters, and hence are useful feature selection methodologies for the analysis of bioinformatics data.