Naïve Bayes and SVM classifiers for classifying databank accession number sentences from online biomedical articles

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7534, Issue , 2010, Pages

  Kim, Jongwoo ^a   Le, Daniel X ^a   Thoma, George R ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Bibliographic information; Databank; Labeling; Na ve Bayes; Support Vector Machine (SVM); Text classification

Indexed keywords

ACCURACY RATE; BAYES CLASSIFIER; BIBLIOGRAPHIC INFORMATION; CLASSIFICATION PERFORMANCE; DATA BANK; F-MEASURE; HARMONIC MEAN; MERGING OPERATORS; SVM CLASSIFIERS; TEXT CLASSIFICATION;

BIOMEDICAL ENGINEERING; CLASSIFIERS; INFORMATION RETRIEVAL; LABELING; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

CLASSIFICATION (OF INFORMATION);

EID: 77949799386     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.838961     Document Type: Conference Paper

References (21)

1. "Technical Memorandum 347: Databank Accession Numbers," National Institutes of Health, National Library of Medicine, October, 1993.
2. th IEEE Symposium on Computer-Based Medical Systems, pp. 905-910, 2006.
3. D. D. Lewis, "Naïve (Bayes) at Forty: The Independence Assumption in Information Retrieval," ECML, The Tenth European Conference on Machine Learning, pp.4-15, 1998.
4. A. McCallum and K. Nigam, "A Comparison of Event Models for Naïve Bayes Text Classification," Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, pp.577, 1998.
5. D. Madigan, "Statistics and the war on spam," Statistics: A Guide to the Unknown, 4th Ed. (R. Peck, G. Casella, G. Cobb, R. Hoerl, D. Nolan, R. Starbuck and H. Stern, eds.), Thomson Brooks/Cole, Belmont, CA, pp.135-147, 2005.
6. J. D. M. Rennie, L. Shih, J. Teevan, and D. R. Karger, "Tackling the Poor Assumptions of Naïve Bayes Text Classifiers," Proceedings of the Twentieth International Conference on Machine Learning, pp. 616-623, 2003.
7. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
8. th European Conference on Machine Learning, Chemnitz, DE, pp.137-142, 1998
9. th ACM International Conference on Information and Knowledge Management, Washington, pp. 148-155, 1998.
10. E. Gabrilovich and S. Markovitch, "Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5", In ICML'04 pp. 321- 328, 2004.
11. http://www.ncbi.nlm.nih.gov/Genbank/
12. http://clinicaltrials.gov/
13. http://www.ncbi.nlm.nih.gov/geo/
14. http://isrctn.org/
15. http://www.ncbi.nlm.nih.gov/RefSeq/
16. http://www.ncbi.nlm.nih.gov/Omim/
17. http://www.pdb.org/
18. http://pubchem.ncbi.nlm.nih.gov/
19. C. C. Chang and C. J. Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
20. M. Johnson, SVM.NET, 2008. Software available at http://www. matthewajohnson.org/index.html.
21. S. Sohn, W. Kim, D. C. Comeau, and W. J. Wilbur, "Optimal Training Sets for Bayesian Prediction of MeSH Assignment," Journal of the American Medical Informatics Association, Vo. 15, No. 4, pp.546-553, 2008.