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Conference Paper: A cooperative feature gene extraction algorithm that combines classification and clustering

TitleA cooperative feature gene extraction algorithm that combines classification and clustering
Authors
Issue Date2009
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5314287
Citation
The 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW 2009), Washington, DC., 1-4 November 2009. In Conference Proceedings, 2009, p. 197-202 How to Cite?
AbstractIn feature gene selection, filtering model concerns classification accuracy while ignoring gene redundancy problem. On the other hand, gene clustering finds correlated genes without considering their predictive abilities. It is valuable to enhance their performances by the help of each other. We report a new feature gene extraction algorithm, namely Double-thresholding Extraction of Feature Gene (DEFG), that combines gene filtering and gene clustering. It firstly pre-select feature gene set from the original dataset. A modified gene clustering is then applied to refine this set. In the gene clustering, specific designs are employed to balance the predictive abilities and the redundancies of the extracted feature gene. We have tested DEFG on a microarray dataset and compared its performance with that of two benchmark algorithms. The experimental results show that DEFG is superior to them in terms of internal validation accuracy and external validation accuracy. Also, DEFG can generalize the pattern structure by a small number of training samples. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196713
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChow, CK-
dc.contributor.authorZhu, H-
dc.contributor.authorLacy, J-
dc.contributor.authorLingen, MW-
dc.contributor.authorKuo, WP-
dc.contributor.authorChan, K-
dc.date.accessioned2014-04-24T02:10:35Z-
dc.date.available2014-04-24T02:10:35Z-
dc.date.issued2009-
dc.identifier.citationThe 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW 2009), Washington, DC., 1-4 November 2009. In Conference Proceedings, 2009, p. 197-202-
dc.identifier.isbn978-142445121-0-
dc.identifier.urihttp://hdl.handle.net/10722/196713-
dc.description.abstractIn feature gene selection, filtering model concerns classification accuracy while ignoring gene redundancy problem. On the other hand, gene clustering finds correlated genes without considering their predictive abilities. It is valuable to enhance their performances by the help of each other. We report a new feature gene extraction algorithm, namely Double-thresholding Extraction of Feature Gene (DEFG), that combines gene filtering and gene clustering. It firstly pre-select feature gene set from the original dataset. A modified gene clustering is then applied to refine this set. In the gene clustering, specific designs are employed to balance the predictive abilities and the redundancies of the extracted feature gene. We have tested DEFG on a microarray dataset and compared its performance with that of two benchmark algorithms. The experimental results show that DEFG is superior to them in terms of internal validation accuracy and external validation accuracy. Also, DEFG can generalize the pattern structure by a small number of training samples. ©2009 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5314287-
dc.relation.ispartofIEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 Proceedings-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 Proceedings. Copyright © IEEE.-
dc.titleA cooperative feature gene extraction algorithm that combines classification and clustering-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBMW.2009.5332126-
dc.identifier.scopuseid_2-s2.0-72849106566-
dc.identifier.spage197-
dc.identifier.epage202-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160602 amended-

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