Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)

Parameter identification in Choquet Integral by the Kullback-Leibler divergence on continuous densities with application to classification fusion

Authors
Emmanuel Ramasso, Sylvie Jullien
Corresponding Author
Emmanuel Ramasso
Available Online August 2011.
DOI
https://doi.org/10.2991/eusflat.2011.69How to use a DOI?
Keywords
Information fusion, Fuzzy measures, Relative Entropy, Health assessment, Classification
Abstract
Classifier fusion is a means to increase accuracy and decision-making of classification systems by designing a set of basis classifiers and then combining their outputs. The combination is made up by non linear functional dependent on fuzzy measures called Choquet integral. It constitues a vast family of aggregation operators including minimum, maximum or weighted sum. The main issue before applying the Choquet integral is to identify the 2M - 2 parameters for M classifiers. We follow a previous work by Kojadinovic and one of the authors where the identification is performed using an informationtheoritic approach. The underlying probability densities are made smooth by fitting continuous parametric and then the Kullback-Leibler divergence is used to identify fuzzy measures. The proposed framework is applied on widely used datasets.
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Proceedings
Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2011
ISBN
978-90-78677-00-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2011.69How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Emmanuel Ramasso
AU  - Sylvie Jullien
PY  - 2011/08
DA  - 2011/08
TI  - Parameter identification in Choquet Integral by the Kullback-Leibler divergence on continuous densities with application to classification fusion
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 132
EP  - 139
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.69
DO  - https://doi.org/10.2991/eusflat.2011.69
ID  - Ramasso2011/08
ER  -