Class imbalances versus class overlapping: An analysis of a learning system behavior

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2972, Issue , 2004, Pages 312-321

  Prati, Ronaldo C ^a   Batista, Gustavo E A P A ^a   Monard, Maria C ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; DATA ACQUISITION; DATABASE SYSTEMS; LEARNING ALGORITHMS; PROBLEM SOLVING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

CLASS IMBALANCES; CLASS OVERLAPPING; DATA SETS; CLASS IMBALANCE; LOSS OF PERFORMANCE; REAL WORLD DOMAIN; SYSTEMATIC STUDY;

LEARNING SYSTEMS; LEARNING ALGORITHMS;

EID: 9444270977     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24694-7_32     Document Type: Conference Paper

References (12)

1. N. Chawla, N. Japkowicz, and A. Kolcz, editors. ICML '2003 Workshop on Learning from Imbalanced Data Sets (II), 2003. Proceedings available at http://www.site.uottawa.ca/~nat/Workshop2003/workshop2003.html.
2. C. Drummond and R. C. Holt. Explicity representing expected cost: An alternative to roc representation. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 198-207, 2000.
3. C. Ferri, P. Flach, and J. Hernández-Orallo. Learning decision trees using the area under the ROC curve. In C. S. A. Huffman, editor, Nineteenth International Conference on Machine Learning (ICML-2002), pages 139-146. Morgan Kaufmann Publishers, 2002.
4. D. J. Hand. Construction and Assessment of Classification Rules. John Wiley and Sons, 1997.
5. N. Japkowicz, editor. AAAI Workshop on Learning from Imbalanced Data Sets, Menlo Park, CA, 2003. AAAI Press. Techical report WS-00-05.
6. N. Japkowicz. Class imbalances: Are we focusing on the right issue? In Proc. of the ICML'2003 Workshop on Learning from Imbalanced Data Sets (II), 2003.
7. N. Japkowicz and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5):429-450, 2002.
8. J. Laurikkala. Improving Identification of Difficult Small Classes by Balancing Class Distributions. Technical Report A-2001-2, University of Tampere, Finland, 2001.
9. C. J. Merz and P. M. Murphy. UCI Repository of Machine Learning Datasets, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
10. F. J. Provost and T. Fawcett. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In Knowledge Discovery and Data Mining, pages 43-48, 1997.
11. J. R. Quinlan. C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
12. G. M. Weiss and F. Provost. The Effect of Class Distribution on Classifier Learning: An Empirical Study. Technical Report ML-TR-44, Rutgers University, Department of Computer Science, 2001.