Combining Classifiers for HIV-1 Drug Resistance Prediction

Title: Combining Classifiers for HIV-1 Drug Resistance Prediction

Volume: 15 Issue: 5

Author(s): Anantaporn Srisawat and Boonserm Kijsirikul

Affiliation:

Keywords: SVM, the RBF network, k-NN, HIV-1 drug resistance prediction, combining classifiers, composite classifier

Abstract: This paper applies and studies the behavior of three learning algorithms, i.e. the Support Vector machine (SVM), the Radial Basis Function Network (the RBF network), and k-Nearest Neighbor (k-NN) for predicting HIV-1 drug resistance from genotype data. In addition, a new algorithm for classifier combination is proposed. The results of comparing the predictive performance of three learning algorithms show that, SVM yields the highest average accuracy, the RBF network gives the highest sensitivity, and k-NN yields the best in specificity. Finally, the comparison of the predictive performance of the composite classifier with three learning algorithms demonstrates that the proposed composite classifier provides the highest average accuracy.

Cite this article as:

Srisawat Anantaporn and Kijsirikul Boonserm, Combining Classifiers for HIV-1 Drug Resistance Prediction, Protein & Peptide Letters 2008; 15 (5) . https://dx.doi.org/10.2174/092986608784567537

DOI https://dx.doi.org/10.2174/092986608784567537	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305