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
Journal ArticleDOI
Using measures of similarity and inclusion for multiple classifier fusion by decision templates
TL;DR: The equivalence is proven by showing that for every object submitted for classification, all four measures induce the same ordering on the set of class labels (through DT fusion), thereby assigning the object to the same class.
Journal ArticleDOI
Shape-Based Averaging
T. Rohlfing,C.R. Maurer +1 more
TL;DR: It is concluded that SBA improves the contiguity and accuracy of averaged image segmentations and was more robust for small numbers of atlases and for low atlas resolutions when combined with shape-based interpolation.
Journal ArticleDOI
The combination of multiple classifiers using an evidential reasoning approach
TL;DR: This paper proposes a 'class-indifferent' method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination, and establishes a range of formulae for combining these mass functions in order to arrive at a consensus decision.
Journal ArticleDOI
Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
TL;DR: A feature selection technique using genetic algorithms is applied for classification, and crisp and fuzzy k-nearest neighbor (kNN) classifiers are compared and Composite fuzzy classifier architectures are investigated.
Journal ArticleDOI
Application of Dempster—Shafer theory in condition monitoring applications: a case study
TL;DR: It is argued that the use of predictive accuracy for basic probability assignments can improve the overall system performance when compared to `traditional' mass assignment techniques.
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.