Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data

TL;DR

This paper introduces a novel approach to improve naive Bayes regression models by generating optimized artificial training data through population-based algorithms, leading to better generalization performance.

Contribution

It presents a new method of enhancing naive Bayes regression by evolving artificial surrogate data, a novel twist on traditional training paradigms.

Findings

Artificial data improves naive Bayes regression accuracy

Population-based algorithms effectively generate surrogate data

Enhanced models outperform those trained on real data

Abstract

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.