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Title: To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators
Author: Yang, Ying
Webb, Geoffrey I.
Cerquides Bueno, Jesús
Korb, Kevin B.
Boughton, Janice
Ting, Kai Ming
Keywords: Estadística bayesiana
Complexitat computacional
Teoria de l'estimació
Aprenentatge automàtic
Reconeixement de formes (Informàtica)
Bayes methods
Computational complexity
Estimation theory
Learning (artificial intelligence)
Pattern classification
Statistical testing
Issue Date: 2007
Publisher: IEEE
Abstract: We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. 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 which have an 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.
Note: Reproducció del document publicat a
It is part of: IEEE Transactions on Knowledge and Data Engineering, 2007, vol. 19, núm. 12, p. 1652-1665.
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ISSN: 1041-4347
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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