A generic classifier-ensemble approach for biomedical named entity recognition Liao, Zhihua and Zhang, Zili 2012, A generic classifier-ensemble approach for biomedical named entity recognition. In Tan, Pang-Ning, Chawla, Sanjay, Ho, Chin Kuan and Bailey, James (ed), Advances in knowledge discovery and data mining, Springer-Verlag, Berlin, Germany, pp.86-97. Attached Files Name Description MIMEType Size Downloads Title A generic classifier-ensemble approach for biomedical named entity recognition Author(s) Liao, Zhihua Zhang, Zili orcid.org/0000-0002-8721-9333 Title of book Advances in knowledge discovery and data mining Editor(s) Tan, Pang-Ning Chawla, Sanjay Ho, Chin Kuan Bailey, James Publication date 2012 Series Lecture notes in artificial intelligence; vol. 7301 Chapter number 8 Total chapters 50 Start page 86 End page 97 Total pages 12 Publisher Springer-Verlag Place of Publication Berlin, Germany Summary In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to put into a classifier-ensemble is a crucial issue of multiple classifier-ensemble design. With this observation in mind, we proposed a generic genetic classifier-ensemble method for the classifier selection in biomedical NER. Various diversity measures and majority voting are considered, and disjoint feature subsets are selected to construct individual classifiers. A basic type of individual classifier – Support Vector Machine (SVM) classifier is adopted as SVM-classifier committee. A multi-objective Genetic algorithm (GA) is employed as the classifier selector to facilitate the ensemble classifier to improve the overall sample classification accuracy. The proposed approach is tested on the benchmark dataset – GENIA version 3.02 corpus, and compared with both individual best SVM classifier and SVM-classifier ensemble algorithm as well as other machine learning methods such as CRF, HMM and MEMM. The results show that the proposed approach outperforms other classification algorithms and can be a useful method for the biomedical NER problem. Notes Presented at the 16th Pacific-Asia Conference, PAKDD 2012 Kuala Lumpur, Malaysia, May 29 – June 1, 2012 Proceedings, Part I ISBN 3642302173 9783642302176 ISSN 0302-9743 1611-3349 Language eng Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 080199 Artificial Intelligence and Image Processing not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter Persistent URL http://hdl.handle.net/10536/DRO/DU:30049537 Document type: Book Chapter Collections: School of Information Technology ERA Postprints Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 29 Nov 2012, 07:55:18 EST Mon, 03 Dec 2012, 10:39:40 EST Mon, 03 Dec 2012, 10:40:41 EST Mon, 10 Dec 2012, 08:55:41 EST Wed, 22 May 2013, 16:38:09 EST Thu, 23 May 2013, 08:57:08 EST Wed, 02 Jul 2014, 14:10:53 EST Fri, 08 Aug 2014, 10:12:38 EST Wed, 21 Oct 2015, 14:37:56 EST Thu, 05 Apr 2018, 14:13:18 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 277 Abstract Views, 15 File Downloads  -  Detailed Statistics Created: Thu, 29 Nov 2012, 07:55:18 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.