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
Proceedings ArticleDOI
Identifying significant genes from microarray data
Han-Yu Chuang,Hongfang Liu,Stuart M. Brown,Cameron McMunn-Coffran,Cameron McMunn-Coffran,Cheng-Yan Kao,D.F. Hsu +6 more
TL;DR: A framework for selecting informative genes, called ranking and combination analysis (RAC), which combines various existing informative gene selection methods is described, which shows that the RAC framework is a robust and efficient approach to identify informative gene for microarray data.
Book ChapterDOI
Selection of Classifiers Based on Multiple Classifier Behaviour
TL;DR: Results on the classification of different data sets show that dynamic classifier selection based on MCS behaviour is an effective operation mechanism for MCSs.
Journal ArticleDOI
Texture classification system using colour space fusion
TL;DR: In this article, a novel colour texture classification system is presented based on an ensemble of independent classifiers each assigned to a different color representation model, and Gaussian Markov random fields features are used in a study to illustrate the previously unexplored approach and its potential to combine information from different colour spaces to improve accuracy.
Proceedings ArticleDOI
An approach for bearing fault diagnosis based on PCA and multiple classifier fusion
Min Xia,Fanrang Kong,Fei Hu +2 more
TL;DR: The result of experiments show that this new bearing fault diagnosis system recognize different working conditions of bearing more accurately and more stably than a single classifier does, which demonstrates the high efficiency of the proposed system.
Journal ArticleDOI
Use of Dempster-Shafer theory to combine classifiers which use different class boundaries
TL;DR: The Dempster-Shafer theory is presented as a framework within which the results of a Bayesian network classifier and a fuzzy logic-based classifier are combined to produce a better final classification.
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.