a noise tolerant fine tuning algorithm for the naïve bayesian learning algorithm

Clicks: 179
ID: 153888
2014
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Abstract
This work improves on the FTNB algorithm to make it more tolerant to noise. The FTNB algorithm augments the Naïve Bayesian (NB) learning algorithm with a fine-tuning stage in an attempt to find better estimations of the probability terms involved. The fine-tuning stage has proved to be effective in improving the classification accuracy of the NB; however, it makes the NB algorithm more sensitive to noise in a training set. This work presents several modifications of the fine tuning stage to make it more tolerant to noise. Our empirical results using 47 data sets indicate that the proposed methods greatly enhance the algorithm tolerance to noise. Furthermore, one of the proposed methods improved the performance of the fine tuning method on many noise-free data sets.
Reference Key
hindi2014journala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Khalil El Hindi
Journal journal of heritage tourism
Year 2014
DOI
10.1016/j.jksuci.2014.03.008
URL
http://www.sciencedirect.com/science/article/pii/S1319157814000093
Keywords
Machine learning overfitting computer science

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