Quantification and semi-supervised classification methods for handling changes in class distribution

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

Volumn , Issue , 2009, Pages 897-905

  Xue, Jack Chongjie ^a   Weiss, Gary M ^a  

^a FORDHAM UNIVERSITY   (United States)

Author keywords

Class distribution; Classification; Concept drift; Quantification; Semi supervised learning

Indexed keywords

CLASS DISTRIBUTION; CLASSIFICATION; CONCEPT DRIFT; QUANTIFICATION; SEMI-SUPERVISED LEARNING;

CLASSIFIERS;

SUPERVISED LEARNING;

EID: 70350681186     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557117     Document Type: Conference Paper

References (20)

1. Alaiz-Rodriguez, R., Guerrero-Curieses, A. and Cid-Sueiro, J. Minimax Regret Classifier for Imprecise Class Distribu-tions. Journal of Machine Learning Research, 8 (2007), 103-130.
2. Alaiz-Rodriguez, R. and Japkowicz, N. Assessing the Im-pact of Changing Environments on Classifier Performance. In the Proceedings of the 21st Canadian Conference in Ar-tificial Intelligence (2008), 13-24.
3. Asuncion, A. and Newman, D. J. UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/ MLReposito-ry.html (2007).
4. Chapelle, O., Scholkopf, B. and Zien, A. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
5. Chawla, N. V., Bowyer, K. W. and Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16 (2002), 321-357.
6. Elkan, C. The foundations of cost-sensitive learning. In the Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001), 973-978.
7. Forman, G. Quantifying counts and costs via classification. Data Mining Knowledge Discovery, 17, 2 (2008), 164-206.
8. Forman, G. Counting Positives Accurately Despite Inaccu-rate Classification. In ECML (2005), 564-575.
9. Latinne, P., Saerens, M. and Decaestecker, C. Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing. In the Pro-ceedings of the 18th International Conference on Machine Learning (2001), 298-305.
10. Provost, F. and Fawcett, T. Robust Classification for Impre-cise Environments. Machine Learning, 42, 3 (2001), 203-231.
11. Provost, F., Fawcett, T. and Kohavi, R. The Case against Accuracy Estimation for Comparing Induction Algorithms. In the Proceedings of the 15th International Conference on Machine Learning (1998), 445-453.
12. Quinlan, R. J. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
13. Saerens, M., Latinne, P. and Decaestecker, C. Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure. Neural Computing, 14 (2002), 14-21.
14. Tsymbal, A. The problem of concept drift: Definitions and related work. Computer Science Department, Trinity Col-lege Dublin, 2004.
15. Weiss, G. M. Mining with rarity: a unifying framework. SIGKDD Explorations Newsletter, 6, 1 (2004), 7-19.
16. Weiss, G. M. and Provost, F. Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research, 19 (2003), 315-354.
17. Witten, I. H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, June 2005.
18. Yang, C. and Zhou, J. Non-stationary data sequence classifi-cation using online class priors estimation. Pattern Recogni-tion, 41, 8 (2008), 2656-2664.
19. Zadrozny, B. and Elkan, C. Learning and making decisions when costs and probabilities are both unknown. In the Pro-ceedings of the 7th International Conference on Knowledge Discovery and Data Mining (2001), 204-213.
20. Zhu, X. Semi-Supervised Learning Literature Survey. Com-puter Science Department, University of Wisconsin-Madison, 2005.