Combining Instance Weighting and Fine
Tuning for Training Naïve Bayesian Classifiers with Scant Training Data
Khalil El Hindi
Department
of Computer Science, King Saud University, Saudi Arabia
Abstract: 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.
Keywords: Naïve bayesian
algorithm, classification, machine learning, noisy data sets, instance weighting.
