Enhanced hierarchical classification via isotonic smoothing

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Volumn , Issue , 2008, Pages 151-160

  Punera, Kunal ^a   Ghosh, Joydeep ^b  

^a YAHOO RESEARCH   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Dynamic programming; Hierarchical classification; Regualrized isotonic regression; Taxonomy

Indexed keywords

ACTIVE AREAS; AUTOMATED CLASSIFICATION SYSTEMS; CLASSIFICATION ACCURACIES; DO-MAINS; EMPIRICAL ANALYSES; HIERARCHICAL CLASSIFICATION; HIERARCHICAL CLASSIFICATIONS; POST-PROCESSING; REGUALRIZED ISOTONIC REGRESSION; SPACE DECOMPOSITIONS; TEXT CLASSIFICATIONS; TREE REGRESSIONS;

CLASSIFIERS; DYNAMIC PROGRAMMING; HIERARCHICAL SYSTEMS; INTERNET; LEARNING SYSTEMS; REGRESSION ANALYSIS; SYSTEMS ENGINEERING; TAXONOMIES; TREES (MATHEMATICS); WORLD WIDE WEB;

TEXT PROCESSING;

EID: 57349192755     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367497.1367518     Document Type: Conference Paper

References (19)

1. S. Angelov, B. Harb, S. Kannan, and L.-S. Wang. Weighted isotonic regression under the L1 norm. In Proc. of Annual ACM-SIAM Symposium on Discrete Algorithms, pages 783-791, 2006.
2. D. Chakrabarti, R. Kumar, and K. Punera. Page-level template detection via isotonic smoothing. In Proc. of International Conference on World Wide Web, pages 61 -70. ACM Press, 2007.
3. S. Chakrabarti, B. Dom, R. Agrawal, and P. Raghavan. Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. VLDB Journal: Very Large Data Bases, 7(3): 163-178, 1998.
4. S. Chakrabarti, B. Dom, and P. Indyk. Enhanced hypertext categorization using hyperlinks. In Proc. of ACM SIGMOD International Conference on Management of Data, pages 307-318. ACM Press, 1998.
5. DMOZ. Open directory project, http://www.dmoz.org.
6. S. Dumais and H. Chen. Hierarchical classification of web content. In Proc. of Annual International ACM Conference on Research and Development in Information Retrieval, pages 256-263. ACM Press, 2000.
7. T. Fawcett. ROC graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4, Hewlett Packard Laboratories, Jan. 17 2003.
8. T.-K. Huang, R. C. Weng, and C.-J. Lin. Generalized Bradley-Terry models and multi-class probability estimates. Journal of Machine Learning Research, 7:85-115, 2006.
9. T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proc. of European Conference on Machine Learning, pages 137-142, 1998.
10. D. Koller and M. Sahami. Hierarchically classifying documents using very few words. In Proc. of International Conference on Machine Learning, pages 170-178. Morgan Kaufmann Publishers Inc., 1997.
11. A. McCallum, R. Rosenfeld, T. M. Mitchell, and A. Y. Ng. Improving text classification by shrinkage in a hierarchy of classes. In Proc. of International Conference on Machine Learning, pages 359-367. Morgan Kaufmann Publishers Inc., 1998.
12. T. Mitchell. Machine Learning. McGraw Hill, 1997.
13. P. M. Pardalos and G. Xue. Algorithms for a class of isotonic regression problems. Algorithmica, 23(3):211-222, 1999.
14. K. Punera, S. Rajan, and J. Ghosh. Automatic construction of n-ary tree based taxonomies. In Proc. of IEEE International Conference on Data Mining - Workshops, pages 75-79. IEEE Computer Society, 2006.
15. J. Rennie and R. Rifkin. Improving multiclass text classification with the support vector machine. AI Memo AIM-2001-026, Massachusetts Institute of Technology, 2001.
16. Q. Stout. Optimal algorithms for unimodal regression. Computing Science and Statistics, 32:348-355, 2000.
17. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, 2005.
18. Yahoo Web directory http://dir.yahoo.com.
19. B. Zadrozny and C. Elkan. Transforming classifier scores into accurate multiclass probability estimates. In Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 694-699, 2002.