%0 Journal Article %1 Majid:2006:IJHIS %A Majid, Abdul %A Khan, Asifullah %A Mirza, Anwar M. %D 2006 %J International Journal of Hybrid Intelligent Systems %K (AUCH), AUROC Area Convex Hull Support Under algorithms, characteristics classifiers, composite curves, genetic machines, operating optimal programming, receiver the vector %N 2 %P 109--125 %T Combination of support vector machines using genetic programming %U http://iospress.metapress.com/link.asp?id=d5u73e9lpf6493nb %V 3 %X the combination of support vector machine (SVM) classifiers using Genetic Programming (GP) for gender classification problem. In our scheme, individual SVM classifiers are constructed through the learning of different SVM kernel functions. The predictions of SVM classifiers are then combined using GP to develop Optimal Composite Classifier (OCC). In this way, the combined decision space is more informative and discriminant. OCC has shown improved performance than that of optimised individual SVM classifiers using grid search. Another advantage of our GP combination scheme is that it automatically incorporates the issues of optimal kernel function and model selection to achieve high performance classification model. The classification performance is reported by using Receiver Operating Characteristics (ROC) Curve. Experiments are conducted under various feature sets to show that OCC is more informative and robust as compared to their individual SVM classifiers. Specifically, it attains high margin of improvement for small feature sets.

@article{Majid:2006:IJHIS, abstract = {the combination of support vector machine (SVM) classifiers using Genetic Programming (GP) for gender classification problem. In our scheme, individual SVM classifiers are constructed through the learning of different SVM kernel functions. The predictions of SVM classifiers are then combined using GP to develop Optimal Composite Classifier (OCC). In this way, the combined decision space is more informative and discriminant. OCC has shown improved performance than that of optimised individual SVM classifiers using grid search. Another advantage of our GP combination scheme is that it automatically incorporates the issues of optimal kernel function and model selection to achieve high performance classification model. The classification performance is reported by using Receiver Operating Characteristics (ROC) Curve. Experiments are conducted under various feature sets to show that OCC is more informative and robust as compared to their individual SVM classifiers. Specifically, it attains high margin of improvement for small feature sets.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Majid, Abdul and Khan, Asifullah and Mirza, Anwar M.}, biburl = {https://www.bibsonomy.org/bibtex/218120f96267fd1f4cc7f8b5d4973e3db/brazovayeye}, interhash = {c2ce9c4c3864660047d672fea7c8e96e}, intrahash = {18120f96267fd1f4cc7f8b5d4973e3db}, issn = {1448-5869}, journal = {International Journal of Hybrid Intelligent Systems}, keywords = {(AUCH), AUROC Area Convex Hull Support Under algorithms, characteristics classifiers, composite curves, genetic machines, operating optimal programming, receiver the vector}, month = {June}, number = 2, pages = {109--125}, timestamp = {2008-06-19T17:46:13.000+0200}, title = {Combination of support vector machines using genetic programming}, url = {http://iospress.metapress.com/link.asp?id=d5u73e9lpf6493nb}, volume = 3, year = 2006 }