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
Combination of multiple classifiers for post-placement quality inspection of components: A comparative study
TL;DR: Fusion methods of multiple classifiers for improving the classification of individual components in terms of positioning accuracy through computer vision inspection are presented.
Journal ArticleDOI
Classifier combination by bayesian networks for handwriting recognition
TL;DR: In the field of handwriting recognition, classifier combination received much more interest than the study of powerful individual classifiers, mainly due to the enormous variability among classifiers.
Book ChapterDOI
Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm
Vassilis Tampakas,Ioannis E. Livieris,Emmanuel G. Pintelas,Nikos Karacapilidis,Panagiotis Pintelas +4 more
TL;DR: A two-level classification algorithm for predicting students’ graduation time and it identifies with high accuracy the students at risk of not completing their studies and classifies the students based on their expected graduation time.
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Dispersed decision-making system with fusion methods from the rank level and the measurement level – A comparative study
TL;DR: It was found that the use of a dispersed system improved the efficiency of inference of fusion method in most cases.
Book ChapterDOI
On the Quality of Optimal Assignment for Data Association
Jean Dezert,Kaouthar Benameur +1 more
TL;DR: The purpose of this work is not to provide a new algorithm for solving the assignment problem, but a solution to estimate the quality of the individual associations given in the optimal assignment solution.
References
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