Proceedings Article10.1109/ICICIC.2007.214
Classifier Learning Algorithm Based on Genetic Algorithms
Liyan Dong,Guangyuan Liu,Senmiao Yuan,Yongli Li,Zhen Li +4 more
- 05 Sep 2007
- pp 126-126
TL;DR: Experimental result shows that GBAN algorithm performs better than TAN algorithm and has a better accuracy when the relationship between attributes of a data set is relatively complicated.
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Abstract: The paper addresses the problem of classification. A restricted BAN classifier learning algorithm - GBAN based on genetic algorithm is proposed. Genetic algorithm is used in this new algorithm to study the network structure, this can reduce complexity of calculation substantially. Meanwhile, the network structure of TAN classifier is extended by restricting the complexity of the structure of BAN classifier., and then a restricted BAN classifier is obtained. To learn the structure of this kind classifier, fitness function based on logarithm likelihood and the corresponding genetic operator are designed, network structure code scheme is also designed. As a result, this algorithm can converges on the overall optimal structure. Experimental result shows that GBAN algorithm performs better than TAN algorithm and has a better accuracy when the relationship between attributes of a data set is relatively complicated.
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Citations
Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation
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TL;DR: A data-driven Bayesian network classifier named Tree Augmented Naive Bayes (TAN) classifier is developed to identify high-risk foreign vessels coming to the PSC inspection authorities to better identify substandard ships and to allocate inspection resources.
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TL;DR: An optimization algorithm which integrates the genetic algorithm, minimal paths, and Recursive Sum of Disjoint Products is utilized to find the optimal double-component assignment with maximal system reliability.
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Regularized model learning in EDAs for continuous andmulti-objective optimization
Hossein Karshenas
- 19 Jul 2013
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Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
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Moises Goldszmidt
- 14 Jan 2011
TL;DR: The main concepts behind statistical pattern classifiers and Bayesian networks, including the main methods for the automated induction of these models are reviewed, and the advantages of Bayesian network classifiers over other types of classifiers are discussed.
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