Counting positives accurately despite inaccurate classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3720 LNAI, Issue , 2005, Pages 564-575

  Forman, George ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; INFORMATION RETRIEVAL; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING;

MIXTURE MODEL; PROBABILITY ESTIMATORS; TARGET POPULATION;

CLASSIFICATION (OF INFORMATION);

EID: 33646391662     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564096_55     Document Type: Conference Paper

References (6)

1. Bennett, P.: Using Asymmetric Distributions to Improve Text Classifier Probability Estimates. Proc. ACM SIGIR Conference on Research and Development in Information Retrieval, July-August (2003)
2. Fawcett, T.: ROC graphs: Notes and practical considerations for data mining researchers. Tech report HPL-2003-4. Hewlett-Packard Laboratories, Palo Alto, CA, USA (2003)
3. Forman, G.: An Extensive Empirical Study of Feature Selection Metrics for Text Classification. Journal of Machine Learning Research 3 (2003) 1289-1305
4. McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. AAAI/ICML Workshop on Learning for Text Categorization (1998) 41-48
5. Weiss, G., Provost, F.: Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. J. of Artificial Intelligence Research 19 (2003) 315-354
6. Witten, I.H., Eibe Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)