Fig. 1.

Performance of classification algorithms for erroneous versus rare variant k-mer classification. The performance of mentioned classification algorithms for classifying 35-mers are compared over two sets of features. 35-mers are either projected onto a family of (a) 23-mers, 13-mers, and a 13 + 23-mers, and (b) projections onto 15-mers, 15 + 20-mers, 15 + 20 + 25-mers, and 15 + 20 + 25 + 30-mers. The accuracy reported is over fivefold cross validation on 35-mers extracted from HIV viral population. Accuracy improves when 35-mers are projected onto smaller sized k′- mers and as the number of projections increases. Random Forest Classifier has the best accuracy across different classification algorithms.