Combination of support vector machines using genetic programming
International Journal of Hybrid Intelligent Systems
Feature selection for efficient gender classification
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
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In this paper, we are proposing a combination scheme of kernels information of Support Vector Machines (SVMs) for improved classification task using Genetic Programming. In the scheme, first, the predicted information is extracted by SVM through the learning of different kernel functions. GP is then used to develop an Optimal Composite Classifier (OCC) having better performance than individual SVM classifiers. The experimental results demonstrate that OCC is more effective, generalized and robust. Specifically, it attains high margin of improvement at small features. Another side advantage of our GP based intelligent combination scheme is that it automatically incorporates the issues of optimal kernel and model selection to achieve a higher performance prediction model.