@article{
 title = {Classifier subset selection for biomedical named entity recognition},
 type = {article},
 year = {2008},
 identifiers = {[object Object]},
 keywords = {algorithms,biomedical named entity recognition,classifier ensembles,classifier subset selection,genetic,natural language processing,weighted voting},
 pages = {267-282},
 volume = {31},
 websites = {http://www.springerlink.com/index/10.1007/s10489-008-0124-0},
 publisher = {Springer},
 id = {d9d0225b-5aa3-3e52-8638-0987c12ca320},
 created = {2012-12-24T15:02:36.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dimililer2008},
 private_publication = {false},
 abstract = {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 de- veloped. 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 de- veloped 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 usingweighted majority voting. The classifier ensemble formed by the proposed scheme sur- passes 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.},
 bibtype = {article},
 author = {Dimililer, Nazife and Varoğlu, Ekrem and Altınçay, Hakan},
 journal = {Applied Intelligence},
 number = {3}
}

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