Full Citation

Title: Highly Scalable Attribute Selection for Averaged One-Dependence Estimators

Citation Type: Conference Paper

Publication Year: 2014

ISBN:

ISSN:

DOI: 10.1007/978-3-319-06605-9_8

NSFID:

PMCID:

PMID:

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.

Url: http://link.springer.com/10.1007/978-3-319-06605-9_8

User Submitted?: No

Authors: Chen, Shenglei; Martinez, Ana M.; Webb, Geoffrey I.

Conference Name: Pacific-Asia Conference on Knowledge Discovery and Data Mining

Publisher Location: Tainan, Taiwan

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

Countries: