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
Reads0
Chats0
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
More filters
Book ChapterDOI
Rotation-Based Ensemble Classifiers for High-Dimensional Data
TL;DR: This chapter discusses the major issues of M CS, including MCS topology, classifier generation, and classifier combination, providing a summary of MCS applied to remote sensing image classification, especially in high-dimensional data.
Proceedings ArticleDOI
A Feedback-Based Multi-Classifier System
TL;DR: This paper shows a feed-back based multi-classifier system in which the multi- classifier approach is used not only for providing the final decision, but also for improving the performance of the individual classifiers, by means of a closed-loop strategy.
Dissertation
Reliable recognition of handwritten digits using a cascade ensemble classifier system and hybrid features
TL;DR: For the verification of confusing handwritten numeral pairs, the proposed algorithm is used to congregate features, and it outperforms the PCA and compares favorably with other nonparametric discriminant analysis methods.
Proceedings ArticleDOI
Unsupervised learning of neural network ensembles for image classification
TL;DR: Given an initial large set of neural networks, this approach is aimed to select the subset formed by the most error-independent nets, and results show that this approach allows one to design effective neural network ensembles.
Journal ArticleDOI
Information fusion approach to microcalcification characterization
Galina Rogova,Paul C. Stomper +1 more
TL;DR: A hybrid system combining decisions of classifiers utilizing both domain knowledge-based and intensity-based features within the framework of the Evidence theory is introduced, which comprises a hierarchical evidential classifier employing a combination of texture features of individual microcalcifications and a neural network employing cluster features observed by a radiologist.
References
More filters
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Journal ArticleDOI
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.