Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms

Authors:
Nazife Dimililer;Ekrem Varoğlu;Hakan Altınçay
Affiliations:
Department of Computer Engineering, Eastern Mediterranean University, Mağusa, Northern Cyprus, via Mersin-10, Turkey;Department of Computer Engineering, Eastern Mediterranean University, Mağusa, Northern Cyprus, via Mersin-10, Turkey;Department of Computer Engineering, Eastern Mediterranean University, Mağusa, Northern Cyprus, via Mersin-10, Turkey
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Year:
2007

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Abstract

We propose a genetic algorithm for constructing a classifier ensemble using a vote-based classifier selection approach for biomedical named entity recognition task. Assuming that the reliability of the predictions of each classifier differs among classes, the proposed approach is based on dynamic selection of the classifiers by taking into account their individual votes. During testing, the classifiers whose votes are considered as being reliable are combined using weighted majority voting. The classifier ensemble formed by the proposed scheme surpasses the full object F-score of the best individual classifier and the ensemble of all classifiers by 2.5% and 1.3% respectively.