Combination of support vector machines using genetic programming
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- @Article{Majid:2006:IJHIS,
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author = "Abdul Majid and Asifullah Khan and Anwar M. Mirza",
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title = "Combination of support vector machines using genetic
programming",
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journal = "International Journal of Hybrid Intelligent Systems",
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year = "2006",
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volume = "3",
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number = "2",
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pages = "109--125",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Support
vector machines, optimal composite classifiers,
receiver operating characteristics curves, Area Under
the Convex Hull (AUCH), AUROC",
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ISSN = "1448-5869",
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URL = "http://content.iospress.com/articles/international-journal-of-hybrid-intelligent-systems/his00026",
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DOI = "doi:10.3233/HIS-2006-3204",
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size = "17 pages",
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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.",
- }
Genetic Programming entries for
Abdul Majid
Asifullah Khan
Anwar M Mirza
Citations