Decision fusion in neural network ensembles
We present a comparison between different combining techniques in neural network ensembles. The main focus of this paper is on a new architecture that can be used in combining neural network ensembles. This architecture is based on training two neural networks to perform the aggregation. One network...
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creator | Wanas, N.M. Kamel, M.S. |
description | We present a comparison between different combining techniques in neural network ensembles. The main focus of this paper is on a new architecture that can be used in combining neural network ensembles. This architecture is based on training two neural networks to perform the aggregation. One network is trained to establish a confidence factor for each member of the ensemble for every training entry. The other network performs the aggregation of the ensemble to present the final decision. Both these networks evolve together during training. This approach is compared with standard fixed and trained combining schemes. |
doi_str_mv | 10.1109/IJCNN.2001.938847 |
format | Conference Proceeding |
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This approach is compared with standard fixed and trained combining schemes.</description><subject>Clouds</subject><subject>Gaussian distribution</subject><subject>Glass</subject><subject>Image databases</subject><subject>Intelligent networks</subject><subject>Neural networks</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Testing</subject><subject>Voting</subject><issn>1098-7576</issn><issn>1558-3902</issn><isbn>0780370449</isbn><isbn>9780780370449</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2001</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotz81OwzAQBGCLH4m28ABwyh0lrB2v7T2iUGirqlzgXNnJRjKkKYpbId6eiHL6DqMZaYS4lVBICfSwXFWbTaEAZEGlc9qeiYlEdHlJoM7FFKyD0oLWdDEGQC63aM2VmKb0AWDAapqI-yeuY4r7PmuPf8Q-6_k4-G7k8L0fPjPuE-9Cx-laXLa-S3zz70y8P8_fqkW-fn1ZVo_rvJaoDnmDDTndGlReNya40Ej02hBq54KHxiABKS-DV4q5Dco7slIGrMcWoCpn4u60G5l5-zXEnR9-tqeP5S9H0kLn</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Wanas, N.M.</creator><creator>Kamel, M.S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2001</creationdate><title>Decision fusion in neural network ensembles</title><author>Wanas, N.M. ; Kamel, M.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c152t-d5d984f652a4d6b8bd15a4695488ba0d659092a1ba22eefb2a89711b5c84f0523</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Clouds</topic><topic>Gaussian distribution</topic><topic>Glass</topic><topic>Image databases</topic><topic>Intelligent networks</topic><topic>Neural networks</topic><topic>Remote sensing</topic><topic>Satellites</topic><topic>Testing</topic><topic>Voting</topic><toplevel>online_resources</toplevel><creatorcontrib>Wanas, N.M.</creatorcontrib><creatorcontrib>Kamel, M.S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wanas, N.M.</au><au>Kamel, M.S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Decision fusion in neural network ensembles</atitle><btitle>IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)</btitle><stitle>IJCNN</stitle><date>2001</date><risdate>2001</risdate><volume>4</volume><spage>2952</spage><epage>2957 vol.4</epage><pages>2952-2957 vol.4</pages><issn>1098-7576</issn><eissn>1558-3902</eissn><isbn>0780370449</isbn><isbn>9780780370449</isbn><abstract>We present a comparison between different combining techniques in neural network ensembles. The main focus of this paper is on a new architecture that can be used in combining neural network ensembles. This architecture is based on training two neural networks to perform the aggregation. One network is trained to establish a confidence factor for each member of the ensemble for every training entry. The other network performs the aggregation of the ensemble to present the final decision. Both these networks evolve together during training. This approach is compared with standard fixed and trained combining schemes.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2001.938847</doi></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Clouds Gaussian distribution Glass Image databases Intelligent networks Neural networks Remote sensing Satellites Testing Voting |
title | Decision fusion in neural network ensembles |
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