A CFS-based feature weighting approach to naive bayes text classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8681 LNCS, Issue , 2014, Pages 555-562

  Wang, Shasha ^a   Jiang, Liangxiao ^a   Li, Chaoqun ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

complement naive Bayes; feature selection; feature weighting; multinomial naive Bayes; Text classification; the one versus all but one model

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; NEURAL NETWORKS; TEXT PROCESSING;

BENCHMARK DATASETS; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; FEATURE WEIGHTING; MULTINOMIAL NAIVE BAYES; NAIVE BAYES; NAIVE BAYES TEXT CLASSIFIER; TEXT CLASSIFICATION;

CLASSIFIERS;

EID: 84958538703     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11179-7_70     Document Type: Conference Paper

References (15)

1. Yan, J., Gao, X.: Detection and recognition of text superimposed in images base on layered method. Neurocomputing 134, 3-14 (2014)
2. Losada, D. E., Azzopardi, L.: Assessing multivariate Bernoulli models for information retrieval. ACM Transactions on Information Systems (TOIS) 26(3), Article No. 17 (2008)
3. Han, E.-H., Karypis, G.: Centroid-based document classification: Analysis and experimental results. In: Zighed, D. A., Komorowski, J., Z? ytkow, J. M. (eds. ) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 424-431. Springer, Heidelberg (2000)
4. Han, E.-H(S. ), Karypis, G., Kumar, V.: Text Categorization Using Weight Adjusted K-Nearest Neighbor Classification. In: Cheung, D., Williams, G. J., Li, Q. (eds. ) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 53-65. Springer, Heidelberg (2001)
5. Ponte, J. M., Croft, W. B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 275-281, ACM (1998)
6. McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: Working Notes of the 1998 AAAI/ICML Workshop on Learning for Text, 41-48. AAAI Press (1998)
7. Rennie, J. D., Shih, L., Teevan, J., Karger, D. R.: Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning, 616-623. Morgan Kaufmann (2003)
8. Berger, A.: Error-correcting output coding for text classification. In: IJCAI 1999: Workshop on Machine Learning for Information Filtering (1999)
9. Zhang, T., Oles, F. J.: Text categorization based on regularized linear classification methods. Information Retrieval, 5-31 (2001)
10. Jiang, L., Wang, D., Cai, Z.: Discriminatively Weighted Naive Bayes and its Application in Text Classification. International Journal on Artificial Intelligence Tools 21(01), 1250007, 19 (2012)
11. Jiang, L., Cai, Z., Zhang, H., Wang, D.: Naive Bayes Text Classifiers: A Locally Weighted Learning Approach. Journal of Experimental & Theoretical Artificial Intelligence 25(2), 273-286 (2013)
12. Li, Y., Luo, C., Chung, S. M.: Weighted Naive Bayes for Text Classification Using positive Term-Class Dependency. International Journal on Artificial Intelligence Tools 21(01), 1250008, 16 (2012)
13. Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359-366 (2000)
14. Witten, I. H., Frank, E., Hall, M. A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn., p. 978. Morgan Kaufmann (January 2011) ISBN 978-0-12-374856-0
15. Nadeau, C., Bengio, Y.: Inference for the generalization error. Machine Learning 52(3), 239-281 (2003)