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, ZiliORCID iD for 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
ISSN 0302-9743
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

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