Combining classifiers for HIV-1 drug resistance prediction.
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.
Srisawat
Anantaporn
A
Department of Computer Engineering, Chulalongkorn University, Bangkok, 10330. Thailand. anantaporn.s@student.chula.ac.th
Kijsirikul
Boonserm
B
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D024882:Drug Resistance, Viral; D015497:HIV-1; D012680:Sensitivity and Specificity