Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
Lei Xu,Adam Krzyżak,Ching Y. Suen +2 more
- Vol. 22, Iss: 3, pp 418-435
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TLDR
On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.Abstract:
Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >read more
Citations
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Journal ArticleDOI
Combining Non-Parametric Models for Multisource Predictive Forest Mapping
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TL;DR: In this article, the theoretical foundations of Artificial Neural Networks, Decision Trees, and Dempster-Shafer's Evidence Theory are reviewed, compared, and applied to a common data set.
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Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting
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An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination
TL;DR: Seven different fusion methods are discussed, test, and compared: Bayesian, distance-based, Dempster-Shafer, Borda count, decision template, Choquet integral, and context-dependent fusion.
Journal ArticleDOI
Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model
Xuecao Li,Xiaoping Liu,Peng Gong +2 more
TL;DR: An ensemble-urban cellular automata (Ensemble-CA) model to achieve better transition rules and Static validation confirmed that this ensemble framework can achieve better performance in terms of receiver operating characteristic (ROC) statistics and outperformed the best single model.
References
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