Towards enhancing centroid classifier for text classification-A border-instance approach

Neurocomputing

Volumn 101, Issue , 2013, Pages 299-308

  Wang, Deqing ^a   Wu, Junjie ^a   Zhang, Hui ^a   Xu, Ke ^a   Lin, Mengxiang ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Border instance; Centroid based classifier; Iterative adjustment; Support vector machines (SVMs); Text classification categorization

Indexed keywords

BORDER INSTANCE; CLASSIFICATION ACCURACY; COMPUTATIONAL COSTS; GENERALIZATION ACCURACY; ITERATIVE ADJUSTMENT; NATURAL LANGUAGES; TEXT CLASSIFICATION; TEXT CORPORA;

CLASSIFICATION (OF INFORMATION); ITERATIVE METHODS; SUPPORT VECTOR MACHINES;

TEXT PROCESSING;

ACCURACY; ARTICLE; BORDER INSTANCE BASED ITERATIVELY ADJUSTED CENTROID CLASSIFIER; CLASSIFICATION ALGORITHM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; TEXT CLASSIFICATION;

EID: 84868624925     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.08.019     Document Type: Article

References (47)

1. Cervantes Jair, Li Xiaoou, Yu Wen, Li Kang Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing 2008, 71(4-6):611-619.
2. Chih-Chung Chang, Chih-Jen Lin, LIBSVM: A Library for Support Vector Machines, 2001.
3. Chen Pei-Chun, Lee Kuang-Yao, Lee Tsung-Ju, Lee Yuh-Jye, Huang Su-Yun Multiclass support vector classification via coding and regression. Neurocomputing 2010, 73:1501-1512.
4. Cortes Corinna, Vapnik Vladimir Support-vector networks. Mach. Learn. 1995, 20:273-297.
5. Czarnowski Ireneusz Cluster-based instance selection for machine classification. Knowl. Inf. Syst. 2012, 30(1):113-133.
6. Czarnowski Ireneusz, Jedrzejowicz Piotr, Wierzbowska Izabela An A-team approach to learning classifiers from distributed data sources. Int. J. Intell. Inf. Database Syst. 2010, 4(3):245-263.
7. Dietterich Thomas Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:273-297.
8. Hu Guan, Jingyu Zhou, Minyi Guo, A class-feature-centroid classifier for text categorization, in: Proceedings of the 18th International Conference on World Wide Web, 2009, pp. 201-210.
9. Guan Naiyang, Tao Dacheng, Luo Zhigang, Yuan Bo Non-negative patch alignment framework. IEEE Trans. Neural Networks 2011, 22(8):1218-1230.
10. Eui-Hong Han, George Karypis, Centroid-based document classification: analysis & experimental results, in: Proceedings of PKDD'00, 2000.
11. Hsu Chih-Wei, Lin Chih-Jen A comparison of methods for multi-class support vector machines. ACM Trans. Asian Lang. Inf. Process. 2002, 13:415-425.
12. David Hull, Improving text retrieval for the routing problem using latent semantic indexing, in: Proceedings of SIGIR-94, 1994, pp. 282-289.
13. Thorsten Joachims, A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization, in: Proceedings of the 14th International Conference on Machine Learning, 1997, pp. 143-151.
14. Thorsten Joachims, Text Categorization with Support Vector Machines. Technical Report 23, University of Dortmund, 1997.
15. Thorsten Joachims, Text categorization with support vector machines: Learning with many relevant features, in: Proceedings of 10th European Conference on Machine Learning, 1998, pp. 137-142.
16. Sparck Jones Karen A statistical interpretation of term specificity and its application in retrieval. J. Doc. 1972, 28(1):11-21.
17. Lan Man, Tan Chew-Lim, Su Jian, Lu Yue Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(4):721-735.
18. Lertnattee Verayuth, Theeramunkong Thanaruk Effect of term distributions on centroid-based text categorization. Inf. Sci. 2004, 158:89-115.
19. D. David Lewis, Evaluating and optimizing autonomous text classification systems, in: Proceedings of the 18th ACM International Conference on Research and Development in Information Retrieval, Seattle, US, 1995, pp. 246-254.
20. D. David Lewis, E. Robert Schapire, P. James Callan, Ron Papka, Training algorithms for linear text classifiers, in: Proceedings of SIGIR96, 1996, pp. 298-306.
21. Feng Li, Yiming Yang, A loss function analysis for classification methods in text categorization, in: Proceedings of the 20th International Conference on Machine Learning (ICML-2003),Washington DC, 2003, pp. 203-215.
22. Guichong Li, Nathalie Japkowicz, Trevor Stocki, R. Kurt Ungar, Full border identification for reduction of training sets, in: Proceedings of Canadian AI 2008, 2008, pp. 203-215.
23. Yan Liu, Yiming Yang, Jaime Carbonell, Boosting to correct inductive bias in text classification, in: Proceedings of the CIKM, 2002, pp. 348-355.
24. Andrew McCallum, Kamal Nigam, A comparison of event models for Naive Bayes text classification, in: Proceedings of the AAAI Workshop Learning for Text Categorization, 1998.
25. Ross Quinlan John C4.5: Programs for Machine Learning 1993, Morgan Kaufmann Publishers.
26. J. Rocchio, Relevance feedback in information retrieval, in: Salton: The SMART Retrieval System: Experiments in Automatic Document Processing, Prentice Hall, 1971, pp. 313-323.
27. Salton Gerard, Buckley Christopher Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 1988, 24(5):513-523.
28. Salton Gerard, Wong Andrew, Yang Chung-Shu A vector space model for automatic indexing. Commun. ACM 1975, 18(11):613-620.
29. V. David Sanchez A. Advanced support vector machines and kernel methods, Neurocomputing 55 (1-2) (2003) 5-20.
30. Sebastiani Fabrizio Machine learning in automated text categorization. ACM Comput. Surv. 2002, 34(1):1-47.
31. Shrikanth Shankar, George Karypis, Weight adjustment schemes for a centroid based classifier, in: Proceedings of KDD Workshop on Text Mining, 2000.
32. Smola Alex, Schölkopf Bernhard A tutorial on support vector regression. Stat. Comput. 2004, 14(August):199-222.
33. Vincent Tam, Ardi Santoso, Rudy Setiono, A comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization, in: Proceedings of the 16th International Conference on Pattern Recognition, 2002, pp. 235-238.
34. Tan Pang-Ning, Steinbach Michael, Kumar Vipin Introduction to Data Mining 2005, Addison-Wesley.
35. Songbo Tan, Using hypothesis margin to boost centroid text classifier, in: Proceedings of the 2007 ACM Symposium on Applied Computing, 2007, pp. 398-403.
36. Tan Songbo An improved centroid classifier for text categorization. Expert Syst. Appl. 2008, 35(1-2):1279-1285.
37. Tan Songbo, Wang Yuefen, Wu Gaowei Adapting centroid classifier for document categorization. Expert Syst. Appl. 2011, 38(8):10264-10273.
38. Tao Dacheng, Tang Xiaoou, Li Xuelong, Wu Xindong Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28(7):1088-1099.
39. van Rijsbergen C. Information Retrieval 1979, Butterworths, London.
40. M. Wann, T. Hediger, N.N. Greenbaun, The influence of training sets on generalization in feed-forward neural networks, in: Proceedings of the International Joint Conference on Neural Networks, 1990, pp. 137-142.
41. Haoran Wu, Tong-Heng Phang, Bing Liu, Xaioli Li, A refinement approach to handling model misfit in text categorization, in: Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2002, pp. 207-216.
42. Wu Junjie, Xiong Hui, Chen Jian Cog. local decomposition for rare class analysis. Data Min. Knowl. Discovery 2010, 20(2):191-220.
43. Hui Xiong, Junjie Wu, Jian Chen, K-means clustering versus validation measures: a data distribution perspective, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 779-784.
44. Xiong Hui, Wu Junjie, Chen Jian K-means clustering versus validation measures. a data-distribution perspective. IEEE Trans. Syst. Man Cybern. Part B. Cybern. 2009, 39(2):318-331.
45. Yiming Yang, Xin Liu, A re-examination of text categorization methods, in: Proceedings of SIGIR'99, 1999, pp. 42-49.
46. Tianyi Zhou, Dacheng Tao, Multi-label learning via structured decomposition and group sparsity, CoRR, abs/1103.0102:253-263, 2011.
47. Zhou Tianyi, Tao Dacheng, Wu Xindong Manifold elastic net. a unified framework for sparse dimension reduction. Data Min. Knowl. Discovery 2011, 22(3):340-371.