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Improving Naive Bayes Using Class-Conditional ICA

Part of the Lecture Notes in Computer Science book series (LNAI,volume 2527)

Abstract

In the past years, Naive Bayes has experienced a renaissance in machine learning, particularly in the area of information retrieval. This classifier is based on the not always realistic assumption that class-conditional distributions can be factorized in the product of their marginal densities. On the other side, one of the most common ways of estimating the Independent Component Analysis (ICA) representation for a given random vector consists in minimizing the Kullback-Leibler distance between the joint density and the product of the marginal densities (mutual information). From this that ICA provides a representation where the independence assumption can be held on stronger grounds. In this paper we propose class-conditional ICA as a method that provides an adequate representation where Naive Bayes is the classifier of choice. Experiments on two public databases are performed in order to confirm this hypothesis.

Keywords

  • Feature Selection
  • Mutual Information
  • Independent Component Analysis
  • Feature Subset
  • Independent Component Analysis

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bressan, M., Vitrià, J. (2002). Improving Naive Bayes Using Class-Conditional ICA. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_1

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  • DOI: https://doi.org/10.1007/3-540-36131-6_1

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