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DOI: 10.14569/IJACSA.2016.070548
PDF

Conservative Noise Filters

Author 1: Mona M.Jamjoom
Author 2: Khalil El Hindi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 5, 2016.

  • Abstract and Keywords
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Abstract: Noisy training data have a huge negative impact on machine learning algorithms. Noise-filtering algorithms have been proposed to eliminate such noisy instances. In this work, we empirically show that the most popular noise-filtering algorithms have a large False Positive (FP) error rate. In other words, these noise filters mistakenly identify genuine instances as outliers and eliminate them. Therefore, we propose more conservative outlier identification criteria that improve the FP error rate and, thus, the performance of the noise filters. With the new filter, an instance is eliminated if and only if it is misclassified by a mutual decision of Naïve Bayesian (NB) classifier and the original filtering criteria being used. The number of genuine instances that are incorrectly eliminated is reduced as a result, thereby improving the classification accuracy.

Keywords: component; Instance Reduction Techniques; Instance-Based Learning; Class noise; Noise Filter; Naive Bayesian; Outlier; False Positive

Mona M.Jamjoom and Khalil El Hindi, “Conservative Noise Filters” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070548

@article{M.Jamjoom2016,
title = {Conservative Noise Filters},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070548},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070548},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Mona M.Jamjoom and Khalil El Hindi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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