Abstract
The Bayes optimal classifier (BOC) is an ensemble technique used extensively in the statistics literature. However, compared to other ensemble techniques such as bagging and boosting, BOC is less known and rarely used in data mining. This is partly due to BOC being perceived as being inefficient and because bagging and boosting consistently outperforms a single model, which raises the question: “Do we even need BOC in datamining?”. We show that the answer to this question is “yes” by illustrating several recent efficient model averaging approximations to BOC can significantly outperform bagging and boosting in realistic situations such as extensive class label noise, sample selection bias and many-class problems. That model averaging techniques outperform bagging and boosting in these situations has not been published in the machine learning, mining or statistical communities to our knowledge.
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References
Buntine, W.: A Theory of Learning Classification Rules, Ph.D. Thesis, UTS (1990)
Davidson, I.: An Ensemble Technique for Stable Learners with Performance Bounds. In: AAAI 2004 (2004)
Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40(2) (2000)
Domingos, P.: Why Does Bagging Work? A Bayesian Account and its Implications. In: KDD 1997 (1997)
Domingos, P.: Bayesian Averaging of Classifiers and the Overfitting Problem. In: AAAI 2000 (2000)
Efron, B.: The jackknife, the bootstrap, and other resampling plans. SIAM Monograph 38 (1982)
Fan, W., Davidson, I., Zadrozny, B., Yu, P.: An Improved Categorization of Classifier’s Sensitivity on Sample Selection Bias. In: ICDM 2005 (2005)
Fan, W., Wang, H., Yu, P.S., Ma, S.: Is random model better? On its accuracy and Efficiency. In: ICDM 2003 (2003)
Liu, F.T., Ting, K.M., Fan, W.: Maximizing Tree Diversity by Building Complete-Random Decision Trees. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518. Springer, Heidelberg (2005)
Frank, Eibe: Personal Communication (2004)
Hoeting, J., Madigan, D., Raftery, A., Volinsky, C.: Bayesian Model Averaging: A Tutorial. Statistical Science 14 (1999)
Kohavi, R., Wolpert, D.: Bias Plus Variance Decomposition for 0-1 Loss Functions. In: ICML 1996 (1996)
McCallum, A.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering (1996), http://www.cs.cmu.edu/~mccallum/bow
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Minka, T.P.: Bayesian model averaging is not model combination, MIT Media Lab note (7/6/2000), http://research.microsoft.com/~minka/papers/bma.html
Rennie, J.: 20 Newsgroups. Technical Report, Dept C.S., MIT (2003)
Zadrozny, B.: Learning and evaluating classifiers under sample selection bias. In: ICML 2004 (2004)
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Davidson, I., Fan, W. (2006). When Efficient Model Averaging Out-Performs Boosting and Bagging. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_46
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DOI: https://doi.org/10.1007/11871637_46
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