Combination of evidence-based classifiers for text categorization

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2011, Pages 422-429

  Bi, Yaxin ^a   Wu, Shengli ^a   Wang, Hui ^a   Guo, Gongde ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b FUJIAN NORMAL UNIVERSITY   (China)

Author keywords

Evidential fusion; Multiple classifier systems; Text categorization

Indexed keywords

COMPARATIVE ANALYSIS; DATA SETS; DEMPSTER-SHAFER THEORY OF EVIDENCE; DICHOTOMOUS STRUCTURE; MAJORITY VOTING; MODEL BASED APPROACH; MULTIPLE CLASSIFIER SYSTEMS; NEAREST NEIGHBOUR; REUTERS-21578; ROCCHIO; TEXT CATEGORIZATION; TEXT CLASSIFIERS; TEXT CORPORA;

ARTIFICIAL INTELLIGENCE; FORMAL LOGIC; LEARNING ALGORITHMS; TEXT PROCESSING;

SUPPORT VECTOR MACHINES;

EID: 84862946446     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2011.69     Document Type: Conference Paper

References (17)

1. J. A. Aslam and M. H. Montague (2001). Models for metasearch. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 275-284.
2. L. Baker and A. K. McCallum (1998). Distributional clustering of words for text classification. In In Proc of 21st ACM International Conference on Research and Development in Information Retrieval, 96-103.
3. J. Barnett (1991). Combining opinions about the order of rule execution. In AAAI, 477-481.
4. P. Bennett, S. Dumais, and E. Horvitz (2005). The combination of text classifiers using reliability indicators. Information Retrieval, 8(1):67-100. (Pubitemid 40965679)
5. Y. Bi, D. Bell, H. Wang, G. Guo, and J. Guan (2007). Combining multiple classifiers using dempster-shafer's rule for text categorization. Applied Artificial Intelligence, 21(1):211-239. (Pubitemid 46417501)
6. Y. Bi, J. Guan, and D. Bell (2008). The combination of multiple classifiers using an evidential reasoning approach. Artif. Intell., 172(15):1731-1751.
7. C. C. Chang and C. J. Lin (2001). Libsvm: a library for support vector machines. In [http://www.csie.ntu.edu.tw/cjlin/libsvm], 2001.
8. G. Shafer (1978). A Mathematical Theory of Evidence. Princeton University Press.
9. G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer (2003). knn model-based approach in classification. In Cooperative Information Systems (CoopIS) International Conference., 986-996.
10. D. J. Ittner, D. D. Lewis, and D. D. Ahn (1995). Text categorization of low quality images. In In Symposium on Document Analysis and Information Retrieval, 301-315.
11. T. Joachims (1997). A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning, 143-151.
12. J. M. Kittler, Hatef, R. Duin, and J. Matas (1998). On combining classifiers. IEEE Trans on pattern Analysis and Machine Intelligence, 20(3):226-239. (Pubitemid 128741312)
13. V. Plachouras and I. O. V. Plachouras (2005). Dempster-shafer theory for a query-biased combination of evidence on the web. Journal Information Retrieval, 8(2):197-218. (Pubitemid 40526281)
14. R. E. Schapire and Y. Singer (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135-168. (Pubitemid 30594821)
15. F. Sebastiani (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47.
16. L. Xu, A. Krzyzak, and C. Y. Suen (1992). Several methods for combining multiple classifiers and their applications in handwritten character recognition. IEEE Trans. on System, Man and Cybernetics, 22(3): 418-435.
17. Y. Yang (2001). A study on thresholding strategies for text categorization. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, 137-145. (Pubitemid 32951066)