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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Early classification of network traffic through multi-classification
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Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks
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Combining rough decisions for intelligent text mining using Dempster's rule
TL;DR: A boosting-like technique for generating multiple sets of rules based on rough set theory and model classification decisions from multiple set of rules as pieces of evidence which can be combined by Dempster’s rule of combination is developed.
Book ChapterDOI
A comparative evaluation of fusion strategies for multimodal biometric verification
TL;DR: A new strategy is proposed and discussed in order to generate a multimodal combined score by means of Support Vector Machine (SVM) classifiers from which user-independent and user-dependent fusion schemes are derived and evaluated.
References
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