@article{Majid:2006:IJHIS,
title = {Combination of support vector machines using genetic
programming},
author = {Abdul Majid and Asifullah Khan and Anwar M. Mirza},
journal = {International Journal of Hybrid Intelligent Systems},
month = {June},
number = {2},
pages = {109--125},
url = {http://iospress.metapress.com/link.asp?id=d5u73e9lpf6493nb},
volume = {3},
year = {2006},
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.},
issn = {1448-5869},
keywords = {(AUCH), AUROC Area Convex Hull Support Under algorithms, characteristics classifiers, composite curves, genetic machines, operating optimal programming, receiver the vector }
}
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