A generic classifier-ensemble approach for biomedical named entity recognition

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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.

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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:	Faculty of Science, Engineering and Built Environment School of Information Technology ERA Postprints 2018 ERA Submission


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