TY - GEN
T1 - Computational intelligence for HIV modelling
AU - Betechuoh, Brain Leke
AU - Marwala, Tshilidzi
AU - Manana, Jabulile V.
PY - 2008
Y1 - 2008
N2 - In this paper, we compare computational intelligence methods to analyze HIV in order to investigate which network is best suited for HIV classification. The methods analyzed are autoencoder multi-layer perceptron (MLP), autoencoder radial basis functions (RBF), support vector machines (SVM) and neuro-fuzzy models (NFM). The autoencoder multi-layer perceptron yields the highest accuracy of 92% amongst all the models studied. The autoencoder radial basis function model has the shortest computational time but yields one of the lowest accuracies of 82%. The SVM model yields the worst accuracy of 80%, as well as the worst computational time of 203s. The NFM yields an accuracy of 86%, which is the second highest accuracy. The NFM, however, offers rules, which gives interpretation of the data. The area under the receiver operating characteristics curve for the MLP model is 0.86 compared to an area under the curve of 0.87 for the RBF model, and 0.82 for the neuro-fuzzy model. The autoencoder MLP network model for HIV classification, is thus found to outperform the other network models and is a much better classifier.
AB - In this paper, we compare computational intelligence methods to analyze HIV in order to investigate which network is best suited for HIV classification. The methods analyzed are autoencoder multi-layer perceptron (MLP), autoencoder radial basis functions (RBF), support vector machines (SVM) and neuro-fuzzy models (NFM). The autoencoder multi-layer perceptron yields the highest accuracy of 92% amongst all the models studied. The autoencoder radial basis function model has the shortest computational time but yields one of the lowest accuracies of 82%. The SVM model yields the worst accuracy of 80%, as well as the worst computational time of 203s. The NFM yields an accuracy of 86%, which is the second highest accuracy. The NFM, however, offers rules, which gives interpretation of the data. The area under the receiver operating characteristics curve for the MLP model is 0.86 compared to an area under the curve of 0.87 for the RBF model, and 0.82 for the neuro-fuzzy model. The autoencoder MLP network model for HIV classification, is thus found to outperform the other network models and is a much better classifier.
UR - http://www.scopus.com/inward/record.url?scp=50249189187&partnerID=8YFLogxK
U2 - 10.1109/INES.2008.4481281
DO - 10.1109/INES.2008.4481281
M3 - Conference contribution
AN - SCOPUS:50249189187
SN - 9781424420834
T3 - 12th International Conference on Intelligent Engineering Systems - Proceedings, INES 2008
SP - 127
EP - 132
BT - 12th International Conference on Intelligent Engineering Systems - Proceedings, INES 2008
T2 - 12th International Conference on Intelligent Engineering Systems, INES 2008
Y2 - 25 February 2008 through 29 February 2008
ER -
