Abstract
In this work, we present a semi-naive Bayes classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the naive Bayes classifier in a very robust way and reaches the performance of the Pazzani’s semi-naive Bayes [1] without the high cost of a wrapper search.
This work has been supported by the Spanish ‘Consejer’ía de Innovación, Ciencia y Empresa de la Junta de Andalucía’ and ‘Ministerio de Educación y Ciencia’, under Projects TIC-276 and TIN2004-06204-C03-02 and FPU scholarship (AP2004-4678).
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Abellán, J., Cano, A., Masegosa, A.R., Moral, S. (2007). A Semi-naive Bayes Classifier with Grouping of Cases. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_43
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DOI: https://doi.org/10.1007/978-3-540-75256-1_43
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