%0 Conference Paper %1 ChenEtAl14 %A Chen, S. %A Martinez, A. %A Webb, G.I. %B Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining %D 2014 %K Conditional Estimation,AODE Probability %P 86-97 %T Highly Scalable Attribute Selection for AODE %U http://dx.doi.org/10.1007/978-3-319-06605-9_8 %X 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.

@inproceedings{ChenEtAl14, abstract = {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.}, added-at = {2016-03-20T05:42:04.000+0100}, author = {Chen, S. and Martinez, A. and Webb, G.I.}, biburl = {https://www.bibsonomy.org/bibtex/2d7c2de212b78dc92507b8fa61560d60f/giwebb}, booktitle = {Proceedings of the 18th {Pacific}-{Asia} Conference on Knowledge Discovery and Data Mining}, interhash = {b3ef4827bb240af960300fc7b3169f79}, intrahash = {d7c2de212b78dc92507b8fa61560d60f}, keywords = {Conditional Estimation,AODE Probability}, pages = {86-97}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Highly Scalable Attribute Selection for AODE}, url = {http://dx.doi.org/10.1007/978-3-319-06605-9_8}, year = 2014 }