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학술지 Associatvie Naive Bayes Classifier: Automated Linking of Gene Ontology to Medline Documents
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저자
김현기, Su Shing Chen
발행일
200909
출처
Pattern Recognition, v.42 no.9, pp.1777-1785
ISSN
0031-3203
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.patcog.2009.01.020
협약과제
09MS3100, 웹 QA 기술개발, 장명길
초록
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 제안 키워드
Gene ontology, Knowledge Representation, MEDLINE documents, Mining method, NB classifier, Support VectorMachine(SVM), classification methodology, document classification, naive Bayes classifier, text mining