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Mathematical Problems in Engineering
Volume 2012, Article ID 793490, 24 pages
http://dx.doi.org/10.1155/2012/793490
Research Article

Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

School of Automation, Beijing Institute of Technology, Beijing 100081, China

Received 31 December 2011; Revised 30 March 2012; Accepted 26 April 2012

Academic Editor: Serge Prudhomme

Copyright © 2012 Lei La et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Al-Mubaid and S. A. Umair, “A new text categorization technique using distributional clustering and learning logic,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 9, pp. 1156–1165, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Lan, C. L. Tan, J. Su, and Y. Lu, “Supervised and traditional term weighting methods for automatic text categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 721–735, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Lan, C. L. Tan, and H. B. Low, “Proposing a new term weighting scheme for text categorization,” in Proceedings of the 21st National Conference on Artificial Intelligence, pp. 763–768, July 2006. View at Scopus
  4. L. Galavotti, F. Sebastiani, and M. Simi, “Experiments on the use of feature selection and negative evidence in automated text categorization,” in Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries, pp. 59–68, 2000.
  5. P. Soucy and G. W. Mineau, “Beyond tfidf weighting for text categorization in the vector space model,” in Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI '05), pp. 1130–1135, August 2005.
  6. X. Quan, L. Wenyin, and B. Qiu, “Term weighting schemes for question categorization,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 1009–1021, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Liu, J. Bian, and E. Agichtein, “Predicting information seeker satisfaction in community question answering,” in Proceedings of the 31st Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR '08), pp. 483–490, July 2008. View at Scopus
  8. Y. Yang and J. O. Pedersen, “A comparative study on feature selection in text categorization,” in Proceedings of the 14th International Conference on Machine Learning (ICML '97), pp. 412–420, 1997.
  9. Y. Chao, W. Yuanqing, L. Jiuxue, and Z. Zhaoyang, “Theory deduction of AdaBoost classification,” Journal of Southeast University, vol. 41, no. 4, 2011. View at Google Scholar
  10. R. Qahwaji, M. Al-Omari, T. Colak, and S. Ipson, “Using the real, gentle and modest AdaBoost learning algorithms to investigate the computerised associations between coronal mass ejections and filaments,” in Proceedings of the International Conference on Communications, Computers and Applications, pp. 37–42, Bradford, UK, August 2008. View at Scopus
  11. R. E. Schapire and Y. Singer, “Improved boosting algorithms using confidence-rated predictions,” Machine Learning, vol. 37, no. 3, pp. 297–336, 1999. View at Publisher · View at Google Scholar · View at Scopus
  12. R. E. Schapire, M. Rochery, M. Rahim, and N. Gupta, “Boosting with prior knowledge for call classification,” IEEE Transactions on Speech and Audio Processing, vol. 13, no. 2, pp. 174–181, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, “A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data,” in Applications of Data Mining in Computer Security, D. Barbara and S. Jajodia, Eds., Kluwer, Norwell, Mass, USA, 2002. View at Google Scholar
  14. W. Hu, W. Hu, and S. Maybank, “AdaBoost-based algorithm for network intrusion detection,” IEEE Transactions on Systems, Man, and Cybernetics B., vol. 38, no. 2, pp. 577–583, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. K. M. Ting and Z. Zheng, “Boosting cost-sensitive trees,” in Proceedings of the 1st International Conference on Discovery Science, pp. 244–255, Springer, December 1998.
  16. F. U. Zhong-Liang, “Cost-sensitive AdaBoost algorithm for multi-class classication problems,” Journal of Automation, vol. 37, no. 8, pp. 973–983, 2011. View at Publisher · View at Google Scholar
  17. E. Song, D. Huang, G. Ma, and C. C. Hung, “Semi-supervised multi-class Adaboost by exploiting unlabeled data,” Expert Systems with Applications, vol. 38, no. 6, pp. 6720–6726, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. I. Maglogiannis, H. Sarimveis, C. T. Kiranoudis, A. A. Chatziioannou, N. Oikonomou, and V. Aidinis, “Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 1, pp. 42–54, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and N. Kumar, “Support-vector-based fuzzy neural network for pattern classification,” IEEE Transactions on Fuzzy Systems, vol. 14, no. 1, pp. 31–41, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. J. M. Yang, P. T. Yu, and B. C. Kuo, “A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data,” IEEE Transactions on Geoscience and Remote Sensing, no. 3, pp. 1279–1293, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semi-arid landscape endmembers using spectral angle mapper (SAM) algorithm,” in Proceedings of the Summaries of the 4th Annual JPL Airborne Geoscience Workshop, vol. 1, pp. 147–150, AVIRIS Workshop, R. Green, Ed., Pasadena, Calif, USA, October 1992.
  22. Z. Chun-hong and X. Wei, “The approach to text automatic classification technology of characteristic catabases on the SVM-KNN,” Information Science, vol. 11, 2009. View at Google Scholar
  23. D. Coomans and D. L. Massart, “Alternative k-nearest neighbour rules in supervised pattern recognition. Part 2. Probabilistic classification on the basis of the kNN method modified for direct density estimation,” Analytica Chimica Acta, vol. 138, pp. 153–165, 1982. View at Google Scholar · View at Scopus
  24. N. Boonyanunta and P. Zeephongsekul, “Improving the predictive power of AdaBoost: a case study in classifying borrowers,” in Proceeding of the 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE '03), pp. 674–685, June 2003. View at Scopus
  25. H. J. Lin, Y. T. Kao, F. W. Yang, and P. S. P. Wang, “Content-based image retrieval trained by adaboost for mobile application,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 20, no. 4, pp. 525–541, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Mitéran, J. Matas, E. Bourennane, M. Paindavoine, and J. Dubois, “Automatic hardware implementation tool for a discrete adaboost-based decision algorithm,” Eurasip Journal on Applied Signal Processing, vol. 2005, no. 7, pp. 1035–1046, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. L. P. Dinu and A. Rusu, “Rank distance aggregation as a fixed classifier combining rule for text categorization,” Lecture Notes in Computer Science, vol. 6008, pp. 638–647, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. S. M. Namburu, T. Haiying, L. Jianhui, and K. R. Pattipati, “Experiments on supervised learning algorithms for text categorization,” in Proceedings of the 2005 IEEE Aerospace Conference, March 2005. View at Scopus
  29. D. Modgil and P. J. La Riviére, “Optimizing wavelength choice for quantitative optoacoustic imaging using the Cramer-Rao lower bound,” Physics in Medicine and Biology, vol. 55, no. 23, pp. 7231–7251, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. A. N. D'Andrea, U. Mengali, and R. Reggiannini, “Modified Cramer-Rao bound and its application to synchronization problems,” IEEE Transactions on Communications, vol. 42, no. 2, pp. 1391–1399, 1994. View at Google Scholar · View at Scopus
  31. M. Sansone, R. Fusco, A. Petrillo, M. Petrillo, and M. Bracale, “An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging,” Medical and Biological Engineering and Computing, vol. 49, no. 4, pp. 485–495, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. G. Lie, G. Ping-Shu, Z. Ming-Heng, L. Lin-Hui, and Z. Yi-Bing, “Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine,” Expert System with Applications, vol. 39, no. 4, pp. 4274–4286, 2012. View at Google Scholar
  33. http://www.sogou.com/labs/resources.html.
  34. L. Fengcheng, H. Degen, and J. Peng, “Chinese word sense disambiguation with AdaBoost.MH algorithm,” Journal of Chinese Information Processing, vol. 20, no. 3, pp. 6–13, 2005. View at Google Scholar
  35. H. Zhi-Kun, G. Wei-Hua, Y. Chun-Hua, D. Peng-cheng, and D. X. Steven, “Text classification method for inverter based on hybrid support vector machines and wavelet analysis,” International Journal of Control Automation and Systems, vol. 9, no. 4, pp. 797–804, 2011. View at Google Scholar
  36. W. Wang and B. Yu, “Text categorization based on combination of modified back propagation neural network and latent semantic analysis,” Neural Computing and Applications, vol. 18, no. 8, pp. 875–881, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. Z. Xuan and T. Da-Gang, “Study on text classification methods,” in Proceedings of the International Conference of China Communication, pp. 123–125, October 2010.