Abstract
The purpose of this research is to propose a feature selection technique for improving the efficiency of dimensionality reduction. The proposed technique is based on a combined Linear SVM Weight with ReliefF. SVM is used as a classifier. The Leukemia and DLBCL dataset from UCI Machine learning Repository were used for our experiments. We discovered that the combined Linear SVM Weight with ReliefF feature selection technique could provide 100 percent accurate result for the model. There was a significant reduction from 5,147 to 20 dimensional data, which is much more efficient than using Linear SVM Weight or ReliefF alone.
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Buathong, W., Meesad, P. (2013). Enhancing the Efficiency of Dimensionality Reduction Using a Combined Linear SVM Weight with ReliefF Feature Selection Method. In: Meesad, P., Unger, H., Boonkrong, S. (eds) The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Advances in Intelligent Systems and Computing, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37371-8_16
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DOI: https://doi.org/10.1007/978-3-642-37371-8_16
Publisher Name: Springer, Berlin, Heidelberg
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