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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A neural network classifier based on Dempster-Shafer theory
TL;DR: A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented, which uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration.
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Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
TL;DR: It is found that inclusion of widely used content related to brand personality is associated with higher levels of consumer engagement (Likes, comments, shares) with a message, and certain directly informative content, such as deals and promotions, drive consumers’ path to conversio...
An Overview of Classifier Fusion Methods
Dymitr Ruta,Bogdan Gabrys +1 more
TL;DR: An overview of classifier fusion methods is given and attempts to identify new trends that may dominate this area of research in future.
Journal ArticleDOI
Switching between selection and fusion in combining classifiers: an experiment
TL;DR: A combination of classifier selection and fusion by using statistical inference to switch between the two by offering a discussion on when to combine classifiers and how classifiers selection (static or dynamic) can be misled by the differences in the classifier team.
Journal ArticleDOI
Combining multiple neural networks by fuzzy integral for robust classification
Sung-Bae Cho,Jong-Sung Kim +1 more
TL;DR: The authors propose a method for multinetwork combination based on the fuzzy integral that nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision.
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