Feature shaping for linear SVM classifiers

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 299-307

  Forman, George ^a   Scholz, Martin ^a   Rajaram, Shyamsundar ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Feature scaling; Feature weighting; Linear support vector machine; Machine learning; SVM; Text classication

Indexed keywords

FEATURE SCALING; FEATURE WEIGHTING; LINEAR SUPPORT VECTOR MACHINE; MACHINE LEARNING; SVM; TEXT CLASSICATION;

CLASSIFIERS; IMAGE RETRIEVAL; LEARNING ALGORITHMS; ROBOT LEARNING; SCALES (WEIGHING INSTRUMENTS); TEXT PROCESSING;

SUPPORT VECTOR MACHINES;

EID: 70350694214     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557057     Document Type: Conference Paper

References (17)

1. D. A. Cieslak and N. V. Chawla. Learning decision trees for unbalanced data. In European Conference on Machine Learning / Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD'08). Springer, 2008.
2. C. Cortes, A. Gretton, G. Lackriet, M. Mohri, and A. Rostamizadeh, editors. NIPS Kernel Learning Workshop. 2008. http://www.cs.nyu.edu/ learning-kernels/.
3. F. Debole and F. Sebastiani. Supervised term weighting for automated text categorization. In SAC '03: Proceedings of the 2003 ACM symposium on Applied computing, pages 784-788, New York, NY, USA, 2003. ACM.
4. G. Forman. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3:1289-1305, 2003.
5. G. Forman. BNS feature scaling: an improved representation over TF-IDF for SVM text classification. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), pages 263-270, NY, 2008.
6. E. Gabrilovich and S. Markovitch. Text categorization with many redundant features: Using aggressive feature selection to make SVMs competitive with C4.5. In Proc. of International Conference on Machine Learning (ICML'04), pages 321-328, 2004.
7. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
8. E. Han and G. Karypis. Centroid-based document classification: Analysis and experimental results. In Proc. of the 4th European Conference on the Principles of Data Mining and Knowledge Discovery, pages 424-431, 2000.
9. H. M. Huan Liu. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Norwell, MA, USA, 1998.
10. H. M. Huan Liu. Computational Methods of Feature Selection. Chapman & Hall/CRC, New York, NY, USA, 2008.
11. A. Knobbe, B. Cremilleux, J. Fürnkranz, and M. Scholz. From local patterns to global models: The LeGo approach to data mining. In Proc. of ECML/PKDD-08 Workshop: From Local Patterns to Global Models (LeGo'08), 2008. http://www.ke.informatik.tu-darmstadt.de/events/LeGo-08/.
12. 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, 99(2), 2008.
13. G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5:27-72, 2004.
14. C. S. Ong and A. J. Smola. Machine learning with hyperkernels. In Proc. of International Conference on Machine Learning (ICML'03), pages 568-575, 2003.
15. J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61-74. MIT Press, 1999.
16. M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy. Composite kernel learning. In Proc. of International Conference on Machine Learning (ICML'08), pages 1040-1047, 2008.
17. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edition, June 2005.