@inproceedings{c527d00c6e7e4a3a979adbd283692bdd,
title = "Hybrid feature selection methods for online biomedical publication classification",
abstract = "We review several feature selection methods: Recursive Feature Elimination, Select K Best, and Random Forests, as elements of a processing chain for feature selection in a text mining task. The text mining task is a multi-label classification problem of label assignment; metadata that is usually applied to published scientific papers by expert curators. In the formulation of this classification task, a feature space that is dramatically larger than the available training data occurs naturally and inevitably. We explore ways to reduce the dimension of the feature space, and show that sequential feature selection does substantially improve performance for this complex type of data.",
keywords = "feature selection, hybrid feature selection, metadata annotation, multi-label classification, text mining",
author = "Long Ma and Yanqing Zhang and Raj Sunderraman and Fox, {Peter T.} and Laird, {Angela R.} and Turner, {Jessica A.} and Turner, {Matthew D.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015 ; Conference date: 12-08-2015 Through 15-08-2015",
year = "2015",
month = oct,
day = "16",
doi = "10.1109/CIBCB.2015.7300320",
language = "English (US)",
series = "2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015",
address = "United States",
}