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Descripción
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| The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. Its accuracy can be improved by relaxing these assumptions. One classifier which does that is the semi-naive Bayes. The state-of-the-art algorithm for learning a semi-naive Bayes from data is the backward sequential elimination and joining (BSEJ) algorithm. We extend BSEJ with a second step which removes some of its unwarranted independence assumptions. Our classifier outperforms BSEJ and five other Bayesian network classifiers on a set of benchmark databases, although the difference in performance is not statistically significant. | |
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Internacional
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No |
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Nombre congreso
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XV Conferencia de la Asociación Española para la Inteligencia Artificial |
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Tipo de participación
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960 |
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Lugar del congreso
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Madrid |
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Revisores
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Si |
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ISBN o ISSN
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978-3-642-40642-3 |
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DOI
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Fecha inicio congreso
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17/09/2013 |
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Fecha fin congreso
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20/09/2013 |
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Desde la página
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159 |
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Hasta la página
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167 |
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Título de las actas
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Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, volume 8109 of Lecture Notes in Computer Science |