Improving text classifier performance based on AUC

Proceedings - International Conference on Pattern Recognition

Volumn 3, Issue , 2006, Pages 268-271

  Wong, Alex K S ^a   Lee, John W T ^a   Yeung, Daniel S ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; GRAPH THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION;

DATA SETS; RECEIVER OPERATING CHARACTERISTICS (ROC); ROC CURVE (AUC); TEXT CLASSIFIER PERFORMANCE;

TEXT PROCESSING;

EID: 34147173286     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.705     Document Type: Conference Paper

References (8)

1. H. Boström. "Maximizing the Area under the ROC Curve using Incremental Reduced Error Pruning", 2nd Workshop on ROC Analysis in ML, hold within the 22nd International Conference on Machine Learning, Germany, Bonn, 2005.
2. U. Brefeld, and T. Scheffer. "AUC Maximizing Support Vector Learning", ROCML05.
3. W. W. Cohen, and Y. Singer, "Context-sensitive learning methods for text categorization", ACM Transactions on Information Systems 13, 1,1999, pp. 100-111.
4. T. Fawcett, "ROC graphs: Notes and practical considerations for researchers", Technical Report HPL-2003-4, HP Labs, 2003.
5. C. Ferri, P. Flach, and J. Hernandez-Orallo. "Learning Decision Trees Using the Area Under the ROC Curve", Proceedings of the 19th International Conference on Machine Learning, Morgan Kaufmann Publishers, July 2002, pp. 139-146.
6. J. Huang, C. X. Ling, "Using AUC and Accuracy in Evaluating Learning Algorithms", IEEE Trans. Knowl. Data Eng. 17(3), 2005, pp. 299-310.
7. D. Lewis, "Reuters-21578 text categoriation test collection", revised at 2004, http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
8. R. C. Prati, and P. A. Flach, "ROCCER: A ROC Convex Hull Rule Learning Algorithm", ROCML05.