@inproceedings{a76854c581a34cdaaf0caa1155aa2174,
title = "Multi-Agent classifiers fusion strategy for biomedical named entity recognition",
abstract = "Recognizing the biomedical named entity has become one of the most fundamental tasks in the biomedical knowledge discovery. The multi-agent classifiers fusion approach proposed here was found to efficiently recognize biomedical named entity. We employ Conditional Random Fields as our underlying classifier model and incorporate diverse set of features into system, the relativity between classifiers is utilized by using co-decision matrix to exchange decision information among classifiers. The experiments are carried on GENIA corpus with the best result of 77.88\% F-sore. The multi-agent classifier fusion strategy proposed here is obviously superior to the individual classifier based method and more effective than the classifiers fusion approach of boosting and bagging.",
author = "Haochang Wang and Tiejun Zhao and Jianmiao Liu",
year = "2008",
doi = "10.1109/BMEI.2008.183",
language = "英语",
isbn = "9780769531182",
series = "BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008",
publisher = "IEEE Computer Society",
pages = "311--315",
booktitle = "BioMedical Engineering and Informatics",
address = "美国",
note = "1st International Conference on BioMedical Engineering and Informatics, BMEI 2008, co-located with the 1st International Congress on Image and Signal Processing, CISP 2008 ; Conference date: 27-05-2008 Through 30-05-2008",
}