ScienceDirect - Data & Knowledge Engineering : Strategies for improving the modeling and interpretability of Bayesian networks

Data & Knowledge Engineering
Volume 63, Issue 1, October 2007, Pages 91-107
Data Warehouse and Knowledge Discovery (DAWAK ’05), 7th International Congress on Data Warehouse and Knowledge Discovery (DAWAK’05)

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doi:10.1016/j.datak.2006.10.005

Strategies for improving the modeling and interpretability of Bayesian networks

Ádamo L. de Santana ^a^,^,, Carlos R. Francês ^a^,, Cláudio A. Rocha ^b^,, Solon V. Carvalho ^c^,, Nandamudi L. Vijaykumar ^c^,, Liviane P. Rego ^a^, and João C. Costa ^a^,

^aLaboratory of High Performance Networks Planning, Federal University of Pará, R. Augusto Côrrea, 01, 66075-110 Belém, PA, Brazil ^bUniversity of the Amazon, Av. Alcindo Cacela, 287, 66060-902 Belém, PA, Brazil ^cLaboratory of Computing and Applied Mathematics, National Institute for Space Research, Av. dos Astronautas 1758, Jd. Granja, 12227-010 São José dos Campos, SP, Brazil

Available online 13 November 2006.

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Abstract

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.

Keywords: Knowledge discovery; Markov chains; Bayesian networks; Multivariate regression