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Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral

Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which...

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Autores principales: Su, Kuo-Lan, Jau, You-Min, Jeng, Jin-Tsong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231602/
https://www.ncbi.nlm.nih.gov/pubmed/22163747
http://dx.doi.org/10.3390/s110302426
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author Su, Kuo-Lan
Jau, You-Min
Jeng, Jin-Tsong
author_facet Su, Kuo-Lan
Jau, You-Min
Jeng, Jin-Tsong
author_sort Su, Kuo-Lan
collection PubMed
description Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers.
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spelling pubmed-32316022011-12-07 Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral Su, Kuo-Lan Jau, You-Min Jeng, Jin-Tsong Sensors (Basel) Article Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers. Molecular Diversity Preservation International (MDPI) 2011-02-25 /pmc/articles/PMC3231602/ /pubmed/22163747 http://dx.doi.org/10.3390/s110302426 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Su, Kuo-Lan
Jau, You-Min
Jeng, Jin-Tsong
Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title_full Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title_fullStr Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title_full_unstemmed Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title_short Modeling of Nonlinear Aggregation for Information Fusion Systems with Outliers Based on the Choquet Integral
title_sort modeling of nonlinear aggregation for information fusion systems with outliers based on the choquet integral
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231602/
https://www.ncbi.nlm.nih.gov/pubmed/22163747
http://dx.doi.org/10.3390/s110302426
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