Ghodke, Sumukh and Timothy Baldwin (2007) An Investigation into the Interaction between Feature Selection and Discretization: Learning How and When to Read Numbers, In Proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI07), Gold Coast, Australia, pp. 48-57

Pre-processing is an important part of machine learning, and has been shown to significantly improve the performance of classifiers. In this paper, we take a selection of pre-processing methods—focusing specifically on discretization and feature selection—and empirically examine their combined effect on classifier performance. In our experiments, we take 11 standard datasets and a selection of standard machine learning algorithms, namely one-R, ID3, naive Bayes, and IB1, and explore the impact of different forms of preprocessing on each combination of dataset and algorithm. We find that in general the combination of wrapper-based forward selection and naive supervised methods of discretization yield consistently above-baseline results.