Inductive model generation for text categorization using a bipartite heterogeneous network

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 1086-1091

  Rossi, Rafael Geraldeli ^a   De Paulo Faleiros, Thiago ^a   De Andrade Lopes, Alneu ^a   Rezende, Solange Oliveira ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Heterogeneous network; Text categorization

Indexed keywords

BAYES ALGORITHMS; CLASSIFICATION MODELS; EMPIRICAL EVALUATIONS; HIGH DIMENSIONALITY; INDUCTIVE MODELS; INDUCTIVE PROCESS; LARGE AMOUNTS; LEAST MEAN SQUARE ALGORITHMS; NUMERIC DATA; PREPROCESSING PHASE; TEXT CATEGORIZATION; TEXT CLASSIFIERS; TEXTUAL DATA; TEXTUAL DOCUMENTS;

ALGORITHMS; HETEROGENEOUS NETWORKS; INFORMATION RETRIEVAL SYSTEMS; TEXT PROCESSING;

DATA MINING;

EID: 84874102893     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.130     Document Type: Conference Paper

References (14)

1. R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, December 2006.
2. C. C. Aggarwal and C. Zhai, Eds., Mining Text Data. Springer, 2012.
3. M. Newman, Networks: An Introduction. New York, NY, USA: Oxford University Press, Inc., 2010.
4. F. Sebastiani, "Machine learning in automated text categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
5. M. Ji, Y. Sun, M. Danilevsky, J. Han, and J. Gao, "Graph regularized transductive classification on heterogeneous information networks," in Principles of Data Mining and Knowledge Discovery, 2010, pp. 570-586.
6. G.-R. Xue, D. Shen, Q. Yang, H.-J. Zeng, Z. Chen, Y. Yu, W. Xi, and W.-Y. Ma, "Irc: An iterative reinforcement categorization algorithm for interrelated web objects," in ICDM'04: Proceeding of the International Conference on Data Mining, 2004, pp. 273-280.
7. Z. Yin, R. Li, Q. Mei, and J. Han, "Exploring social tagging graph for web object classification," in SIGKDD'2009: Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2009, pp. 957-966.
8. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, "Learning with local and global consistency," in NIPS'2003: Proceeding of the Advances in Neural Information Processing Systems, 2003.
9. B. Widrow and M. E. Hoff, "Adaptive switching circuits," in Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, Part 4, 1960, pp. 96-104.
10. G. Forman, "19MclassTextWc dataset," 2006. [Online]. Available: http://sourceforge.net/projects/weka/files/datasets/text-datasets/ 19MclassTextWc.zip/download
11. I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann, 2005.
12. R. Caruana and A. Niculescu-Mizil, "An empirical comparison of supervised learning algorithms," in ICML 2006: Proceeding of the Internation Conference on Machine Learning, 2006, pp. 161-168.
13. T. Kohonen, G. Barna, and R. Chrisley, "Statistical pattern recognition with neural networks: benchmarking studies," in IEEE International Conference on Neural Networks, 1988, pp. 61-68.
14. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.