Strategies for improving the modeling and interpretability of Bayesian networks
| Autor: | João C. W. A. Costa, Ádamo Lima de Santana, Liviane Rego, Solon V. Carvalho, Carlos Renato Lisboa Francês, Claudio Rocha, Nandamudi Lankalapalli Vijaykumar |
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| Rok vydání: | 2007 |
| Předmět: |
Information Systems and Management
Computer science business.industry Bayesian network Machine learning computer.software_genre Variable-order Bayesian network Knowledge extraction Bayesian programming Graphical model Data mining Artificial intelligence Intelligent control business computer Dynamic Bayesian network Interpretability |
| Zdroj: | Data & Knowledge Engineering. 63:91-107 |
| ISSN: | 0169-023X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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