Paper Title:
Towards Efficient Dimensionality Reduction for Evolving Bayesian Network Classifier
  Abstract

Dimensionality reduction is useful for improving the performance of Bayesian networks. In this paper we suggest an effective method of modeling categorical and numerical variables of the mixed data with different Bayesian classifiers. Such an approach reduces output sensitivity to input changes by applying feature extraction and selection, and empirical studies on UCI benchmarking data show that our approach has clear advantages with respect to the classification accuracy.

  Info
Periodical
Advanced Materials Research (Volumes 108-111)
Edited by
Yanwen Wu
Pages
240-243
DOI
10.4028/www.scientific.net/AMR.108-111.240
Citation
L. M. Wang, X. F. Li, X. C. Wang, "Towards Efficient Dimensionality Reduction for Evolving Bayesian Network Classifier", Advanced Materials Research, Vols. 108-111, pp. 240-243, 2010
Online since
May 2010
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Price
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