%0 Journal Article %1 YangWebbCerquideszKorbBoughtonTing07 %A Yang, Y. %A Webb, G.I. %A Cerquides, J. %A Korb, K. %A Boughton, J. %A Ting, K-M. %C Los Alamitos, CA %D 2007 %I IEEE Computer Society %J IEEE Transactions on Knowledge and Data Engineering (TKDE) %K Conditional Estimation,AODE Probability %N 12 %P 1652-1665 %T To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators %V 19 %X We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of semi-naive Bayesian classifiers. Altogether 16 model selection and weighing schemes, 58 benchmark data sets, as well as various statistical tests are employed. This paper�s main contributions are three-fold. First, it formally presents each scheme�s definition, rationale and time complexity; and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme�s classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms with immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

@article{YangWebbCerquideszKorbBoughtonTing07, abstract = {We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of semi-naive Bayesian classifiers. Altogether 16 model selection and weighing schemes, 58 benchmark data sets, as well as various statistical tests are employed. This paper�s main contributions are three-fold. First, it formally presents each scheme�s definition, rationale and time complexity; and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme�s classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms with immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Los Alamitos, CA}, author = {Yang, Y. and Webb, G.I. and Cerquides, J. and Korb, K. and Boughton, J. and Ting, K-M.}, biburl = {https://www.bibsonomy.org/bibtex/27763abb085cb253eeb5355508e0c83db/giwebb}, interhash = {f6e69f225cc3a0d95c69fb051e53f40a}, intrahash = {7763abb085cb253eeb5355508e0c83db}, journal = {{IEEE} Transactions on Knowledge and Data Engineering (TKDE)}, keywords = {Conditional Estimation,AODE Probability}, number = 12, pages = {1652-1665}, publisher = {{IEEE} Computer Society}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators}, volume = 19, year = 2007 }