Improving Naive Bayes for Regression with Optimised Artificial Surrogate
  Data
  Can we evolve better training data for machine learning algorithms? To
investigate this question we use population-based optimisation algorithms to
generate artificial surrogate training data for naive Bayes for regression. We
demonstrate that the generalisation performance of naive Bayes for regression
models is enhanced by training them on the artificial data as opposed to the
real data. These results are important for two reasons. Firstly, naive Bayes
models are simple and interpretable but frequently underperform compared to
more complex "black box" models, and therefore new methods of enhancing
accuracy are called for. Secondly, the idea of using the real training data
indirectly in the construction of the artificial training data, as opposed to
directly for model training, is a novel twist on the usual machine learning
paradigm.
