Abstract
Selecting the proper Kernel function in SVMs and the specific parameters for that kernel is an important step in achieving a high performance learning machine. The objective of this research is to optimize SVMs parameters using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors and the margin define our objective functions. So, we introduce a method based on multi-objective evolutionary algorithm NSGA-II to solve this problem. We also introduce a multi-criteria selection operator for our NSGA-II. The proposed method is applied on some benchmark datasets. The experimental obtained results show the efficiency of the proposed method.
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Bouraoui, A., Ben Ayed, Y., Jamoussi, S. (2014). A Multi-objective Genetic Algorithm for Model Selection for Support Vector Machines. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_64
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DOI: https://doi.org/10.1007/978-3-319-13560-1_64
Publisher Name: Springer, Cham
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