A note on comparing classifiers
Pattern Recognition Letters
A comparison of discriminant procedures for binary variables
Computational Statistics & Data Analysis
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Bayes Optimal Instance-Based Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Generative models for ticket resolution in expert networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Many studies have been made to compare the many different methods of supervised classification which have been developed. While conducting a large meta-analysis of such studies, we spotted some anomalous results relating to the Naive Bayes method. This paper describes our detailed investigation into these anomalies. We conclude that a very large comparative study probably mislabelled another method as Naive Bayes, and that the Statlog project used the right method, but possibly incorrectly reported its provenance. Such mistakes, while not too harmful in themselves, can become seriously misleading if blindly propagated by citations which do not examine the source material in detail.