Abstract
One of the techniques for improving the accuracy of induced classifier is noise filtering. The classifiers prediction performance is affected by the noisy datasets used in the induction of classifiers. Therefore, it is very important to detect and remove the noise in order to increase the classification accuracy. This paper proposed a model for noise detection in the datasets using k-means and support vector machine (SVM) techniques. The proposed model has been tested using the datasets from University of California, Irvine machine learning repository. Experimental results reveal that the proposed model can improve data quality and increase the classification accuracies.
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Acknowledgement
This work is supported by the Ministry of Education and Research Management Centre at the Universiti Teknologi Malaysia under the Research University Grant Scheme (Vote No. Q.J130000.2528.05H84).
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Nematzadeh, Z., Ibrahim, R., Selamat, A. (2015). A Method for Class Noise Detection Based on K-means and SVM Algorithms. In: Fujita, H., Guizzi, G. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2015. Communications in Computer and Information Science, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-22689-7_23
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DOI: https://doi.org/10.1007/978-3-319-22689-7_23
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