MVGPC+: An Integrated Approach to Machine-Learning Classification

C. Aranha and H. Iba (Japan)


Genetic Programming, Classification, Bio-informatics,Financial data, SVM, k-NN, and GP.


We propose a new evolutionary framework called MGVPC+ for the sake of solving classification problems. This approach is an extension of our earlier framework MVGPC, in which a majority voting genetic programming classifier was established with multiple GP runs. In this paper, we integrate GP-based classifiers and other machine learning techniques, e.g., SVM and k-NN methods. The effectiveness of our approach has been confirmed by the experimental results with several classifier task in terms of improved performance and reduced computational burden.

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