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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Patent
System and process for a fusion classification for insurance underwriting suitable for use by an automated system
Piero Patrone Bonissone,Kareem Sherif Aggour,Rajesh Venkat Subbu,Weizhong Yan,Naresh Iyer,Anindya Chakraborty +5 more
TL;DR: In this paper, a method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described, where the outputs of the classifiers are fused and a comparison module is used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, or cases for review.
Journal ArticleDOI
Combination of neural and statistical algorithms for supervised classification of remote-sensing images
TL;DR: The combination of neural and statistical algorithms is proposed as a method to obtain high accuracy values after much shorter design phases and to improve the accuracy–rejection tradeoff over those allowed by single algorithms.
Dissertation
Biologically Inspired Modular Neural Networks
TL;DR: This dissertation presents an in depth study of the currently available modular neural network architectures along with highlighting their shortcomings and investigates new modular artificial neural network models in order to overcome pointed out shortcomings.
Journal ArticleDOI
Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.
TL;DR: The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in the authors' automated breast mass detection scheme.
Journal ArticleDOI
A new hybrid ensemble credit scoring model based on classifiers consensus system approach
Maher Ala’raj,Maysam F. Abbod +1 more
TL;DR: The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against all the base classifiers, hybrid and the traditional combination methods in terms of average accuracy, the area under the curve (AUC) H-measure and the Brier Score.
References
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