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
WCI 04 On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis
A.R.R. Neto,G.A. Barreto +1 more
TL;DR: Results clearly indicate that the ensembles of classifiers have better generalization performance than standalone classifiers when applied to a medical diagnosis problem in the field of orthopedics.
Journal ArticleDOI
Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach
TL;DR: This paper develops a novel classification approach for multiresolution, multisensor [optical and synthetic aperture radar (SAR)], and/or multiband images using a two-step explicit statistical model and integrates a prior update in this model in order to improve the robustness of the developed classifier against noise and speckle.
Journal ArticleDOI
Multi-agent based collaborative fault detection and identification in chemical processes
TL;DR: It is shown that a collaborative FDI approach that combines the strengths of various heterogeneous FDI methods is able to maximize diagnostic performance and is illustrated through fault diagnosis of the startup of a lab-scale distillation unit and the Tennessee Eastman Challenge problem.
Book ChapterDOI
A Framework for Classifier Fusion: Is It Still Needed?
TL;DR: This work considers the problem and issues of classifier fusion and adopts the Bayesian viewpoint and shows how this leads to classifier output moderation to compensate for sampling problems, and elaborate how the final stage of fusion should combine the complementary measurement information that might be available to different experts.
Journal ArticleDOI
Information fusion for computer security: State of the art and open issues
TL;DR: This paper critically review the issue of information fusion for computer security, both in terms of problem formulation and in Terms of state-of-the-art solutions.
References
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