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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Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems
Dymitr Ruta,Bogdan Gabrys +1 more
TL;DR: Diversity measures operating on binary classification outputs (correct/incorrect) are studied and a new diversity measure efficiently exploiting information coming from the whole MCS, rather than its part, for which it is applied is proposed.
Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems
Cheng-Lin Liu,Hiromichi Fujisawa +1 more
TL;DR: The characteristics of the classification methods that have been successfully applied to character recognition are discussed, and the remaining problems that can be potentially solved by learning methods are shown.
Journal ArticleDOI
Classification of microarray cancer data using ensemble approach
TL;DR: This paper presents a method, referred to as SD-EnClass, for combining classifiers from different classification families into an ensemble, based on a simple estimation of each classifier’s class performance, and shows that the proposed model improves classification accuracy.
Proceedings ArticleDOI
Fusion strategies in multimodal biometric verification
TL;DR: A new strategy is proposed and discussed in order to compute a multimodal combined score by means of support vector machine (SVM) classifiers.
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Automatic classification of clustered microcalcifications by a multiple expert system
TL;DR: This paper proposes a novel approach for classifying clusters of microcalcifications, based on a Multiple Expert System; such system aggregates several experts, some of which are devoted to classify the single microCalcifications while others are aimed to classified the cluster considered as a whole.
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