Abstract
The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification and machine learning. In this paper, we propose a new method to find the optimal decision model for SVM, which consists of the minimal set of highly discriminative features and the set of parameters for the kernel. To cope with this problem, we adopted genetic algorithm (GA) which provides efficient optimization tool simulating the natural evolution procedures in iterative fashion to select the optimal set of features and set of kernel parameters. In the method, the decision models generated by GA are evaluated by SVM, and GA selects the only good models and gives the selected models the chance to survive and improve by crossover and mutation operation. Combining GA and SVM, we can obtain the optimal decision model which reduces the execution time as well as improves the classification rate of SVM. We also demonstrated the feasibility of our proposed method by several experiments on the sets of clinical data such as KDD Cup 1999 intrusion detection pattern samples and stomach cancer proteome pattern samples.
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Ohn, SY., Nguyen, HN., Kim, D.S., Park, J.S. (2004). Determining Optimal Decision Model for Support Vector Machine by Genetic Algorithm. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_138
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DOI: https://doi.org/10.1007/978-3-540-30497-5_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
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