Efficient optimization of performance measures by classifier adaptation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 6, 2013, Pages 1370-1382

  Li, Nan ^a,b   Tsang, Ivor W ^c   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

^b SOOCHOW UNIVERSITY   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Classifier adaptation; Curriculum learning; Ensemble learning; Optimize performance measures

Indexed keywords

CONTINGENCY TABLE; EMPIRICAL STUDIES; ENSEMBLE LEARNING; LEARNING METHODS; NONLINEAR CLASSIFIERS; PERFORMANCE MEASURE; QUADRATIC PROGRAMS; TWO-STEP APPROACH;

COMPUTATIONAL EFFICIENCY; CURRICULA; LEARNING SYSTEMS; OPTIMIZATION;

LEARNING ALGORITHMS;

EID: 84885010138     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.172     Document Type: Article

References (34)

1. M. Alvira and R. Rifkin, "An Empirical Comparison of SNoW and SVMs for Face Detection," Technical Report 2001-004, CBCL, MIT, Cambridge, Mass., 2001.
2. Y. Bengio, J. Louradour, R. Collobert, and J. Weston, "Curriculum Learning," Proc. Int'l Conf. Machine Learning, pp. 41-48, 2009.
3. C.M. Bishop, Neural Networks for Pattern Recognition. Oxford Univ. Press, 1995.
4. C.J.C. Burges, R. Ragno, and Q. Le, "Learning to Rank with Nonsmooth Cost Functions," Advances in Neural Information Processing Systems 20, pp. 193-200, 2006.
5. Y. Cao, J. Xu, T.Y. Liu, H. Li, Y. Huang, and H.W. Hon, "Adapting Ranking SVM to Document Retrieval," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 186-193, 2006.
6. R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes, "Ensemble Selection from Libraries of Models," Proc. Int'l Conf. Machine Learning, pp. 18-25, 2004.
7. C.C. Chang and C.J. Lin, "IJCNN 2001 Challenge: Generalization Ability and Text Decoding," Proc. Int'l Joint Conf. Neural Networks, pp. 1031-1036, 2001.
8. C. Cortes and M. Mohri, "AUC Optimization vs. Error Rate Minimization," Advances in Neural Information Processing Systems 16, pp. 313-320, 2004.
9. H. DauméIII and D. Marcu, "Domain Adaptation for Statistical Classifiers," J. Artificial Intelligence Research, vol. 26, pp. 101-126, 2006.
10. L. Duan, I.W. Tsang, D. Xu, and T.S. Chua, "Domain Adaptation from Multiple Sources via Auxiliary Classifiers," Proc. Int'l Conf. Machine Learning, pp. 289-296, 2009.
11. J.L. Elman, "Learning and Development in Neural Networks: The Importance of Starting Small," Cognition, vol. 48, no. 6, pp. 781- 799, 1993.
12. C. Ferri, P. Flach, and J. Hernandez-Orallo, "Learning Decision Trees Using the Area under the ROC Curve," Proc. Int'l Conf. Machine Learning, pp. 139-146, 2002.
13. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten, "The WEKA Data Mining Software: An Update," SIGKDD Exploration Newsletter, vol. 11, no. 1, pp. 10-18, 2009.
14. A. Herschtal and B. Raskutti, "Optimising Area under the ROC Curve Using Gradient Descent," Proc. Int'l Conf. Machine Learning, pp. 49-56, 2004.
15. T. Joachims, "A Support Vector Method for Multivariate Performance Measures," Proc. Int'l Conf. Machine Learning, pp. 377-384, 2005.
16. T. Joachims, T. Finley, and C.-N. J. Yu, "Cutting-Plane Training of Structural SVMs," Machine Learning, vol. 76, no. 1, pp. 27-59, 2009.
17. T. Joachims and C.-N. J. Yu, "Sparse Kernel SVMs via Cutting-Plane Training," Machine Learning, vol. 76, nos. 2/3, pp. 179-193, 2009.
18. J. Lafferty and C. Zhai, "Document Language Models, Query Models, and Risk Minimization for Information Retrieval," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 111-119, 2001.
19. J. Langford and B. Zadrozny, "Estimating Class Membership Probabilities Using Classifier Learners," Proc. Int'l Workshop Artificial Intelligence and Statistics, pp. 198-205, 2005.
20. D.D. Lewis, "Applying Support Vector Machines to the TREC-2001 Batch Filtering and Routing Tasks," Proc. Text REtrieval Conf., pp. 286-292, 2001.
21. X. Li and J. Bilmes, "A Bayesian Divergence Prior for Classifier Adaptation," Proc. Int'l Conf. Artificial Intelligence and Statistics, pp. 275-282, 2007.
22. C.J. Matheus and L.A. Rendell, "Constructive Induction on Decision Trees," Proc. Int'l Joint Conf. Artificial Intelligence, pp. 645-650, 1989.
23. K. Morik, P. Brockhausen, and T. Joachims, "Combining Statistical Learning with a Knowledge-Based Approach-a Case Study in Intensive Care Monitoring," Proc. Int'l Conf. Machine Learning, pp. 268-277, 1999.
24. D.R. Musicant, V. Kumar, and A. Ozgur, "Optimizing F-Measure with Support Vector Machines," Proc. Int'l Florida Artificial Intelligence Research Soc. Conf., pp. 356-360, 2003.
25. J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
26. I.W. Tsang, J.T. Kwok, and P.-M. Cheung, "Core Vector Machines: Fast SVM Training on Very Large Data Sets," J. Machine Learning Research, vol. 6, pp. 363-392, 2005.
27. H. Valizadegan, R. Jin, R. Zhang, and J. Mao, "Learning to Rank by Optimizing NDCG Measure," Advances in Neural Information Processing Systems 22, pp. 1883-1891, 2009,
28. J. Xu, T.Y. Liu, M. Lu, H. Li, and W.Y. Ma, "Directly Optimizing Evaluation Measures in Learning to Rank," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 107-114, 2008.
29. J. Yang and A.G. Hauptmann, "A Framework for Classifier Adaptation and Its Applications in Concept Detection," Proc. ACM SIGMM Int'l Conf. Multimedia Information Retrieval, pp. 467-474, 2008.
30. J. Yang, R. Yan, and A.G. Hauptmann, "Cross-Domain Video Concept Detection Using Adaptive SVMs," Proc. Int'l Conf. Multimedia, pp. 188-197, 2007.
31. C.-N.J. Yu and T. Joachims, "Training Structural SVMs with Kernels Using Sampled Cuts," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 794-802, 2008.
32. Y. Yue, T. Finley, F. Radlinski, and T. Joachims, "A Support Vector Method for Optimizing Average Precision," Proc. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 271-278, 2007.
33. Z.H. Zhou, Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC, 2012.
34. Z.-H. Zhou and Y. Jiang, "NeC4.5: Neural Ensemble Based C4.5," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 6, pp. 770-773, June 2004.