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 Advances in knowledge discovery and data mining, Springer-Verlag, Berlin, Germany, pp.86-97.
Tan, Pang-Ning Chawla, Sanjay Ho, Chin Kuan Bailey, James
Lecture notes in artificial intelligence; vol. 7301
Place of Publication
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.
Presented at the 16th Pacific-Asia Conference, PAKDD 2012 Kuala Lumpur, Malaysia, May 29 – June 1, 2012 Proceedings, Part I
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