International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 124

Research Paper | Information Technology | Indonesia | Volume 4 Issue 9, September 2015


Optimization Features Using GA-SVM Approach

Andy [97] | Michael Fernando | Kristanto Halim | Gradiyanto Sanjaya


Abstract: Feature selection often used to choose the feature that maximizes the prediction of classification accuracy. Feature selection is one of the most important factor that influence classification accuracy rate. In this paper we proposed the combination of Genetic Algorithm (GA) and Support Vector Machine for feature optimization. In this research we compare the result with K Nearest Neighbor, Decision Tree, and Linear Discriminant Analysis. For better comparison, the experiment was conducted using 6 different dataset. The result shows that GA-SVM gives better accuracy than using all features or other method on 3 of 6 dataset.


Keywords: Feature Optimization, Genetic Algorithms GAs, Support Vector Machine SVM


Edition: Volume 4 Issue 9, September 2015,


Pages: 193 - 197


How to Cite this Article?

Andy, Michael Fernando, Kristanto Halim, Gradiyanto Sanjaya, "Optimization Features Using GA-SVM Approach", International Journal of Science and Research (IJSR), Volume 4 Issue 9, September 2015, pp. 193-197, https://www.ijsr.net/get_abstract.php?paper_id=SUB157997

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