Quantifying trends accurately despite classifier error and class imbalance

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

Volumn 2006, Issue , 2006, Pages 157-166

  Forman, George ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Classification; Cost quantification; Quantification; Text mining

Indexed keywords

CLASSIFICATION (OF INFORMATION); COSTS; LEARNING SYSTEMS;

COST QUANTIFICATION; QUANTIFICATION; TEXT MINING;

DATA MINING;

EID: 33749582214     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1150402.1150423     Document Type: Conference Paper

References (12)

1. Fawcett, T. ROC graphs: notes and practical considerations for data mining researchers. Hewlett-Packard Labs, Tech Report HPL-2003-4, 2003. www.hpl.hp.com/techreports
2. Fawcett, T. and Flach, P. A response to Webb and Ting's 'On the application of ROC analysis to predict classification performance under varying class distributions.' Machine Learning, 58(1):33-38, 2005.
3. th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD, Philadelphia), 2006.
4. th European Conf. on Machine Learning (ECML, Porto):564-575, 2005.
5. Forman, G. An extensive empirical study of feature selection metrics for text classification. J. of Machine Learning Research, 3(Mar): 1289-1305, 2003.
6. th European Conf. on the Principles of Data Mining and Knowledge Discovery (PKDD): 424-431, 2000.
7. Havre, S., Hetzler, E., Whitney, P., and Nowell, L. ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1):9-20, 2002.
8. th ACM SIGKDD Int'l Conf. on Knowledge Discovery in Data Mining (KDD, Chicago): 198-207, 2005.
9. Saerens, M., Latinne, P., and Decaestecker, C. Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure. Neural Computation, 14(1):21-41, 2002.
10. th European Conf. on Machine Learning (ECML, Freiburg):527-538, 2001.
11. nd edition), Morgan Kaufmann, San Francisco, CA, 2005.
12. Wu, G. and Chang, E. KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Trans. on Knowledge and Data Engineering, 17(6):786-795, 2005.