DocumentCode :
3135790
Title :
A Novel Naive Bayes Voting Strategy for Combining Classifiers
Author :
De Stefano, Claudio ; Fontanella, Francesco ; Scotto Di Freca, A.
Author_Institution :
Dept. of Electr. & Inf. Eng. (DIEI), Univ. of Cassino & Southern Lazio, Cassino, Italy
fYear :
2012
fDate :
18-20 Sept. 2012
Firstpage :
467
Lastpage :
472
Abstract :
Classifier combination methods have proved to be an effective tool for increasing the performance in pattern recognition applications. The rationale of this approach follows from the observation that appropriately diverse classifiers make uncorrelated errors. Unfortunately, this theoretical assumption is not easy to satisfy in practical cases, thus reducing the performance obtainable with any combination strategy. In this paper we propose a new weighted majority vote rule which try to solve this problem by jointly analyzing the responses provided by all the experts, in order to capture their collective behavior when classifying a sample. Our rule associates a weight to each class rather than to each expert and computes such weights by estimating the joint probability distribution of each class with the set of responses provided by all the experts in the combining pool. The probability distribution has been computed by using the naive Bayes probabilistic model. Despite its simplicity, this model has been successfully used in many practical applications, often competing with much more sophisticated techniques. The experimental results, performed by using three standard databases of handwritten digits, confirmed the effectiveness of the proposed method.
Keywords :
Bayes methods; handwritten character recognition; neural nets; pattern classification; statistical distributions; classifier combination method; handwritten digit; naive Bayes probabilistic model; naive Bayes voting strategy; neural network; pattern recognition; probability distribution; standard database; weighted majority vote rule; Bagging; Boosting; Computational modeling; Joints; Neurons; Reliability; Training; Classifier Ensembles; Combining Rules; Naive Bayes Classifiers; Neural Networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on
Conference_Location :
Bari
Print_ISBN :
978-1-4673-2262-1
Type :
conf
DOI :
10.1109/ICFHR.2012.166
Filename :
6424439
Link To Document :
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