Comparing PCA to information gain as a feature selection method for Influenza-A classification

The paper compares the use of Principal Component Analysis (PCA) to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. Sequences from each segment were further divided into Adamantane-resistant, and non-Adamantane-resistant. Accuracy, sensitivity, specificity precision and time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. The performance also stayed comparable with that of the previous results achieved using IG.