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Journal Article Associative Naïve Bayes classifier: Automated linking of gene ontology to medline documents
Cited 18 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Hyun Ki Kim, Su Shing Chen
Issue Date
2009-09
Citation
Pattern Recognition, v.42, no.9, pp.1777-1785
ISSN
0031-3203
Publisher
Elsevier
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1016/j.patcog.2009.01.020
Abstract
We demonstrate a text-mining method, called associative Na챦ve Bayes (ANB) classifier, for automated linking of MEDLINE documents to gene ontology (GO). The approach of this paper is a nontrivial extension of document classification methodology from a fixed set of classes C = { c1, c2, ..., cn } to a knowledge hierarchy like GO. Due to the complexity of GO, we use a knowledge representation structure. With that structure, we develop the text mining classifier, called ANB classifier, which automatically links Medline documents to GO. To check the performance, we compare our datasets under several well-known classifiers: NB classifier, large Bayes classifier, support vector machine and ANB classifier. Our results, described in the following, indicate its practical usefulness. © 2009 Elsevier Ltd. All rights reserved.
KSP Keywords
Bayes classifier, MEDLINE documents, Mining method, NB classifier, Support VectorMachine(SVM), classification methodology, document classification, gene ontology, knowledge representation, text mining, vector machine(LSSVM)