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Supervised classification using probabilistic decision graphs

Author

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  • Nielsen, Jens D.
  • Rumí, Rafael
  • Salmerón, Antonio

Abstract

A new model for supervised classification based on probabilistic decision graphs is introduced. A probabilistic decision graph (PDG) is a graphical model that efficiently captures certain context specific independencies that are not easily represented by other graphical models traditionally used for classification, such as the Naïve Bayes (NB) or Classification Trees (CT). This means that the PDG model can capture some distributions using fewer parameters than classical models. Two approaches for constructing a PDG for classification are proposed. The first is to directly construct the model from a dataset of labelled data, while the second is to transform a previously obtained Bayesian classifier into a PDG model that can then be refined. These two approaches are compared with a wide range of classical approaches to the supervised classification problem on a number of both real world databases and artificially generated data.

Suggested Citation

  • Nielsen, Jens D. & Rumí, Rafael & Salmerón, Antonio, 2009. "Supervised classification using probabilistic decision graphs," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1299-1311, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1299-1311
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    References listed on IDEAS

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    1. Park, Changyi & Koo, Ja-Yong & Kim, Sujong & Sohn, Insuk & Lee, Jae Won, 2008. "Classification of gene functions using support vector machine for time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2578-2587, January.
    2. Ouali, Abdelaziz & Ramdane Cherif, Amar & Krebs, Marie-Odile, 2006. "Data mining based Bayesian networks for best classification," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1278-1292, November.
    3. Cutillo, L. & Amato, U., 2008. "Localized empirical discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4966-4978, July.
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    Cited by:

    1. Sonia Pérez-Fernández & Pablo Martínez-Camblor & Peter Filzmoser & Norberto Corral, 2021. "Visualizing the decision rules behind the ROC curves: understanding the classification process," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 135-161, March.

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