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Proceedings Paper

Strategies for improving the interpretability of Bayesian networks using Markovian time models and genetic algorithms
Author(s): Ádamo L. de Santana; Cláudio A. Rocha; Carlos R. Francês; Solon V. Carvalho; Nandamudi L. Vijaykumar; João C. W. A. Costa
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Paper Abstract

One of the main factors for the success of the knowledge discovery process is related to the comprehensibility of the patterns discovered by the data mining techniques used. Among the many data mining techniques found in the literature, we can point the Bayesian networks as one of most prominent when considering the easiness of knowledge interpretation achieved in a domain with uncertainty. However, the static Bayesian networks present two basic disadvantages: the incapacity to correlate the variables, considering its behavior throughout the time; and the difficulty of establishing the optimum combination of states for the variables, which would generate and/or achieve a given requirement. This paper presents an extension for the improvement of Bayesian networks, treating the mentioned problems by incorporating a temporal model, using Markov chains, and for intermediary of the combination of genetic algorithms with the networks obtained from the data.

Paper Details

Date Published: 12 October 2006
PDF: 8 pages
Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830R (12 October 2006); doi: 10.1117/12.686413
Show Author Affiliations
Ádamo L. de Santana, Federal Univ. of Para (Brazil)
Cláudio A. Rocha, Univ. of the Amazon (Brazil)
Carlos R. Francês, Federal Univ. of Para (Brazil)
Solon V. Carvalho, National Institute for Space Research (Brazil)
Nandamudi L. Vijaykumar, National Institute for Space Research (Brazil)
João C. W. A. Costa, Federal Univ. of Para (Brazil)


Published in SPIE Proceedings Vol. 6383:
Wavelet Applications in Industrial Processing IV
Frédéric Truchetet; Olivier Laligant, Editor(s)

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