A New Approach to Classifier Fusion Based on Upper Integral

Fusing a number of classifiers can generally improve the performance of individual classifiers, and the fuzzy integral, which can clearly express the interaction among the individual classifiers, has been acknowledged as an effective tool of fusion. In order to make the best use of the individual cl...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics 2014-05, Vol.44 (5), p.620-635
Main Authors: Wang, Xi-Zhao, Wang, Ran, Feng, Hui-Min, Wang, Hua-Chao
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Efficiency measure
Fuzzy
fuzzy integral
Fuzzy logic
Integrals
interaction
Mathematical analysis
Mathematical models
multiple classifier fusion
nonadditive set function
Optimization
upper integral
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fusing a number of classifiers can generally improve the performance of individual classifiers, and the fuzzy integral, which can clearly express the interaction among the individual classifiers, has been acknowledged as an effective tool of fusion. In order to make the best use of the individual classifiers and their combinations, we propose in this paper a new scheme of classifier fusion based on upper integrals, which differs from all the existing models. Instead of being a fusion operator, the upper integral is used to reasonably arrange the finite resources, and thus to maximize the classification efficiency. By solving an optimization problem of upper integrals, we obtain a scheme for assigning proportions of examples to different individual classifiers and their combinations. According to these proportions, new examples could be classified by different individual classifiers and their combinations, and the combination of classifiers that specific examples should be submitted to depends on their performance. The definition of upper integral guarantees such a conclusion that the classification efficiency of the fused classifier is not less than that of any individual classifier theoretically. Furthermore, numerical simulations demonstrate that most existing fusion methodologies, such as bagging and boosting, can be improved by our upper integral model.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2013.2263382