Restricted decontamination for the imbalanced training sample problem

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

Volumn 2905, Issue , 2003, Pages 424-431

  Barandela, R ^a,b   Rangel, E ^a   Sanchez J S ^c   Ferri, F J ^d  

^a INSTITUTO TECNOLÓGICO DE TOLUCA   (Mexico)

^b Instituto de Geografía Tropical   (Cuba)

^c UNIVERSITAT JAUME I   (Spain)

^d UNIVERSITY OF VALENCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DECONTAMINATION; SAMPLING;

CLASSIFICATION ACCURACY; IMBALANCED DATA; SUPERVISED METHODS; TRAINING DATA; TRAINING SAMPLE; WEIGHTED DISTANCE;

PATTERN RECOGNITION;

EID: 35248832239     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24586-5_52     Document Type: Article

References (17)

1. R. Barandela. The Nearest Neighbor rule: an empirical study of its methodological aspects. PhD thesis, Univ. Berlin, 1987.
2. R. Barandela, E. Gasca, and R. Alejo. Correcting the training data. In D. Chen and X. Cheng, editors, Pattern Recognition and String Matching. Kluwer, The Netherlands, 2003.
3. R. Barandela, J. S. Sánchez, and R. M. Valdovinos. New applications of ensembles of classifiers. Pattern Analysis and Applications, to appear, 2003.
4. R. Barandela, J.S. Sánchez, V. García, and E. Rangel. Strategies for learning in class imbalance problems. Pattern Recognition, 36:849-851, 2003.
5. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357, 2000.
6. Merz C. J. and P. M. Murphy. Uci repository of machine learning databases. Technical report, University of California at Irvine, Department of Information and Computer Science, 1998.
7. T. Eavis and N. Japkowicz. A recognition-based alternative to discrimination-based multi-layer perceptrons. In Workshop on Learning from Imbalanced Data Sets. TR WS-00-05, AAAI Press, 2000.
8. K.J. Ezawa, M. Singh, and S.W. Norton. Learning goal oriented bayesian networks for telecommunications management. In Proc. 13th Int. Conf. on Machine Learning, pages 139-147, 1996.
9. T. Fawcett and F. Provost. Adaptive fraud detection. Data Mining and Knowledge Discovery, 1:291-316, 1996.
10. D. J. Hand. Construction and assessment of classification rules. John Wiley and Sons, Chichester, 1997.
11. J. Koplowitz and T. A. Brown. On the relation of performance to editing in nearest neighbor rules. In Proceedings of the 4th International Joint Conference on Pattern Recognition, 1978.
12. M. Kubat, R. Holte, and S. Matwin. Detection of oil-spills in radar images of sea surface. Machine Learning, 30:195-215, 1998.
13. M. Kubat and S. Matwin. Addressing the curse of unbalanced training sets: One-sided selection. In Proceedings of the 14th International Conference on Machine Learning, pages 179-186, 1997.
14. D. Mladenic and M. Grobelnik. Feature selection for unbalanced class distribution and naive bayes. In Proc. 16th Int. Conf. on Machine Learning, pages 258-267, 1999.
15. M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk. Reducing misclassification costs. In Proc 11th Int. Conf. on Machine Learning, pages 217-225, 1994.
16. D. L. Wilson. Asymptotic properties of nearest neighbor rules using edited data. 2(3):408-421, 1972.
17. K. Woods, C. Doss, K.W. Bowyer, J. Solka, C. Priebe, and W.P. Kegelmeyer. Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography. International Journal of Pattern Recognition and Artificial Intelligence, 7:1417-1436, 1993.