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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.
Machine learning, Performance evaluation of algorithms and systems

Y. Yang, G. I. Webb, K. B. Korb, J. Cerquides, J. Boughton and K. M. Ting, "To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators," in IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1652-1665, 2007.
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