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Combining Instance Weighting and Fine Tuning for Training Naïve Bayesian Classifiers with Scant Training Data

Journal: The International Arab Journal of Information Technology (Vol.15, No. 6)

Publication Date:

Authors : ;

Page : 1099-1106

Keywords : Naïve bayesian algorithm; classification; machine learning; noisy data sets; instance weighting.;

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This work addresses the problem of having to train a Naïve Bayesian classifier using limited data. It first presents an improved instance-weighting algorithm that is accurate and robust to noise and then it shows how to combine it with a fine tuning algorithm to achieve even better classification accuracy. Our empirical work using 49 benchmark data sets shows that the improved instance-weighting method outperforms the original algorithm on both noisy and noise-free data sets. Another set of empirical results indicates that combining the instance-weighting algorithm with the fine tuning algorithm gives better classification accuracy than using either one of them alone.

Last modified: 2019-04-30 21:40:40