Averaged One-Dependence Estimators (AODE) is a popular and effective approach to Bayesian learning. In this paper, a new attribute selection approach is proposed for AODE. It can search in a large model space, while it requires only a single extra pass through the training data, resulting in a computationally efficient two-pass learning algorithm. The experimental results indicate that the new technique significantly reduces AODE's bias at the cost of a modest increase in training time. Its low bias and computational efficiency make it an attractive algorithm for learning from big data. © 2014 Springer International Publishing.
CITATION STYLE
Chen, S., Martinez, A. M., & Webb, G. I. (2014). Highly scalable attribute selection for averaged one-dependence estimators. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8444 LNAI, pp. 86–97). Springer Verlag. https://doi.org/10.1007/978-3-319-06605-9_8
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