Certainty-enhanced active learning for improving imbalanced data classification

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 405-412

  Fu, Jui Hsi ^a   Lee, Sing Ling ^a  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

Active learning; Certainty enhanced neighborhood; Imbalanced data classification; Lazy learning; Streaming datasets

Indexed keywords

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; CERTAINTY- ENHANCED NEIGHBORHOOD; DATA SETS; ERROR MINIMIZATION; IMBALANCED DATA; LAZY LEARNING; REAL-WORLD DATASETS; UNLABELED SAMPLES;

DATA MINING; LEARNING ALGORITHMS;

CLASSIFICATION (OF INFORMATION);

EID: 84863182737     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2011.43     Document Type: Conference Paper

References (30)

1. H. Chen, Y.Zhan, and Y. Li. The application of decision tree in Chinese email classification. In International Conference on Machine Learning and Cybernetics, pp. 305-308, Qingdao, China, July 2010.
2. S. Chakravarthy, A. Venkatachalam, and A. Telang. A Graph- Based Approach for Multi-folder Email Classification. In IEEE International Conference on Data Mining, pp. 78-87, Sydney, Australia, December 2010.
3. F. Dehghani, N. Movahhedinia, M.R. Khayyambashi, and S. Kianian. Real-Time Traffic Classification Based on Statistical and Payload Content Features. In International Workshop on Intelligent Systems and Applications, pp. 1-4, Wuhan, China, May 2010.
4. T. Joachims. Transductive Inference for Text Classification Using Support Vector Machines. In International Conference on Machine Learning, pp. 200-209, Bled, Slovenia, June 1999.
5. K. Nigam, A.K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning - Special issue on information retrieval, 39(2-3): 103-134, 2000.
6. E.B. Baum and K. Lang. Query learning can work poorly when a human oracle is used. In IEEE International Conference on Joint Conference on Neural Networks, 1992.
7. S. Ertekin, J. Huang, L. Bottou, and L. Giles. Learning on the Border: Active Learning in Imbalanced Data Classification. In ACM Conference on Information and Knowledge Management, pp. 127-136, Lisboa, Portugal, November 2007.
8. M. Li an I.K. Sethi. Confidence-based active learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(8): 1251-1261, 2006.
9. M.F. Balcan, A. Beygelzimer, and J. Langford. Agnostic active learning. In International Conference on Machine Learning, pp. 65-72, Pennsylvania, USA, June 2006.
10. S. Hanneke. A bound on the Label Complexity of Agnostic Active Learning. In International Conference on Machine Learning, pp. 353-360, Oregon, USA, June 2007.
11. S. Dasgupta, A.T. Kalai, and C. Monteleoni. Analysis of Perceptron-Based Active Learning. Machine Learning Research, 10:281-299, 2009.
12. A. Beygelzimer, S. Dasgupta, and J. Langford. Importance weighted active learning. In International Conference on Machine Learning, pp. 49-56, Montreal, Canada, June 2009.
13. A. Beygelzimer, D. Hsu, J. Langford, and T. Zhang. Agnostic Active Learning without Constraints. In Neural Information Processing Systems, Vancouver, Canada, December 2010.
14. S. Dasgupta. Coarse sample complexity bounds for active learning. In Neural Information Processing Systems, 2005.
15. M.L. Zhang and Z.H. Zhou. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7): 2038-2048, 2007.
16. A. Veloso, W. Meira, Jr., M. Cristo, M. Goncalves, and M. Zaki. Multi-evidence, multi-criteria, lazy associative document classification. In ACM International Conference on Information and knowledge management, pp. 218-227, VA, USA, November 2006.
17. Y. Sun, M.S. Kamel, A.K.C. Wong, and Y. Wang. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition, 40(12): 3358-3378, 2007.
18. V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
19. L.M. Manevitz and M. Yousef. One-class svms for document classification. Machine Learning Research, 2:139-154, 2002.
20. N. Japkowicz. Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning, 42(1-2): 97-122, 2001.
21. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Artificial Intelligence Research, 16(1): 321-357, 2002.
22. M. Kubat and S. Matwin. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In International Conference on Machine Learning, pp. 179-186, Tennessee, USA, July 1997.
23. J. He, H. Tong, and J. Carbonell. Rare Category Characterization. In IEEE International Conference on Data Mining, pp. 226-235, Sydney, Australia, December 2010.
24. B. Liu, J. Yin, Y.S. Xiao, L.B. Cao, and P.S. Yu. Exploiting Local Data Uncertainty to Boost Global Outlier Detection. In IEEE International Conference on Data Mining, pp. 304-313, Sydney, Australia, December 2010.
25. Y. Tang, Y.Q. Zhang, N.V. Chawla, and S. Krasser. SVMs Modeling for Highly Imbalanced Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics- Special issue on human computing, 39(1): 281-288, 2009.
26. J.H. Fu and S.L. Lee. A Multi-Strategy Approach to KNN and LARM on Small and Incrementally Induced Prediction Knowledge. In Advanced Data Mining and Applications, Lecture Notes in Computer Science, pp. 453-460, 2009.
27. X. Zhu. Lazy Bagging for Classifying Imbalanced Data. In IEEE International Conference on Data Mining, pp. 763-768, Omaha NE, USA, October 2007.
28. T.D. Vries, S. Chawla, and M.E. Houle. Finding Local Anomalies in Very High Dimensional Space. In IEEE International Conference on Data Mining, pp. 128-137, Sydney, Australia, December 2010.
29. T. Zhang. Data dependent concentration bounds for sequential prediction algorithms. In Learning Theory, Lecture Notes in Computer Science, pp. 173-187, 2005.
30. Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001.