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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Improving algorithms for structure learning in Bayesian Networks using a new implicit score
Expert Systems with Applications, Volume 37, No. 7, Year 2010
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Description
Learning Bayesian Network structure from database is an NP-hard problem and still one of the most exciting challenges in machine learning. Most of the widely used heuristics search for the (locally) optimal graphs by defining a score metric and employs a search strategy to identify the network structure having the maximum score. In this work, we propose a new score (named implicit score) based on the Implicit inference framework that we proposed earlier. We then implemented this score within the K2 and MWST algorithms for network structure learning. Performance of the new score metric was evaluated on a benchmark database (ASIA Network) and a biomedical database of breast cancer in comparison with traditional score metrics BIC and BD Mutual Information. We show that implicit score yields improved performance over other scores when used with the MWST algorithm and have similar performance when implemented within K2 algorithm. © 2010 Elsevier Ltd. All rights reserved.
Authors & Co-Authors
Bouchaala, Lobna
Tunisia, Sfax
Centre de Biotechnologie de Sfax
Masmoudi, Afif
Tunisia, Sfax
Faculté Des Sciences de Sfax
Gargouri, Faïez
Tunisia, Sfax
Institut Supérieur D'informatique et de Multimédia de Sfax
REBAI, AHMED
Tunisia, Sfax
Centre de Biotechnologie de Sfax
Statistics
Citations: 57
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1016/j.eswa.2010.02.065
ISSN:
09574174
Research Areas
Cancer