Abstract
Classification is usually the final and one of the most important steps in most of the tasks involving machine learning, computer vision, etc., for e.g., face detection, optical character recognition, etc. This paper gives a novel technique for estimating the performance of Naïve Bayes Classifier in noisy data. It also talks about removing those attributes that cause the classifier to be biased toward a particular class.
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Rawat, K., Kumar, A., Gautam, A.K. (2014). Lower Bound on Naïve Bayes Classifier Accuracy in Case of Noisy Data. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_68
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DOI: https://doi.org/10.1007/978-81-322-1602-5_68
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