Atlantis Press Paper Details: Parameter identification in Choquet Integral by the Kullback-Leibler divergence on continuous densities with application to classification fusion

title:

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

publication:

EUSFLAT

part of series:

Advances in Intelligent Systems Research

pages:

132 - 139

DOI:

To be assigned soon (how to use a DOI)

author(s):

Emmanuel Ramasso, Sylvie Jullien

publication date:

July 2011

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.

© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.

full text:

eusflatlfa2011_S2_7.pdf (1602 K)

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title:		Parameter identification in Choquet Integral by the Kullback-Leibler divergence on continuous densities with application to classification fusion
publication:		EUSFLAT
part of series:		Advances in Intelligent Systems Research
pages:		132 - 139
DOI:		To be assigned soon (how to use a DOI)
author(s):		Emmanuel Ramasso, Sylvie Jullien
publication date:		July 2011
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.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		eusflatlfa2011_S2_7.pdf (1602 K)