Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Comparison of character-level and part of speech features for name recognition in biomedical texts
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Improving the performance of dictionary-based approaches in protein name recognition
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Genetic algorithms in classifier fusion
Applied Soft Computing
Bio-medical entity extraction using support vector machines
Artificial Intelligence in Medicine
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Building an annotated corpus in the molecular-biology domain
Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Automatic extraction of acronym definitions from the Web
Applied Intelligence
A novel feature selection method based on normalized mutual information
Applied Intelligence
A generic classifier-ensemble approach for biomedical named entity recognition
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Multi-document summarization via submodularity
Applied Intelligence
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Classifier ensembling approach is considered for biomedical named entity recognition task. A vote-based classifier selection scheme having an intermediate level of search complexity between static classifier selection and real-valued and class-dependent weighting approaches is developed. Assuming that the reliability of the predictions of each classifier differs among classes, the proposed approach is based on selection of the classifiers by taking into account their individual votes. A wide set of classifiers, each based on a different set of features and modeling parameter setting are generated for this purpose. A genetic algorithm is developed so as to label the predictions of these classifiers as reliable or not. During testing, the votes that are labeled 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 by 2.75% and it is the highest score achieved on the data set considered.