Improving k nearest neighbor with exemplar generalization for imbalanced classification

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

Volumn 6635 LNAI, Issue PART 2, 2011, Pages 321-332

  Li, Yuxuan ^a   Zhang, Xiuzhen ^a  

^a RMIT UNIVERSITY   (Australia)

Author keywords

Cost sensitive learning; imbalanced learning; kNN; re sampling

Indexed keywords

DATA MINING; MOTION COMPENSATION; TEXT PROCESSING;

COST-SENSITIVE LEARNING; IMBALANCED CLASS; IMBALANCED CLASSIFICATION; IMBALANCED LEARNING; K-NEAREST NEIGHBOR CLASSIFIERS (KNN); K-NEAREST NEIGHBORS; NEAREST NEIGHBORS; RESAMPLING;

NEAREST NEIGHBOR SEARCH;

EID: 79957969472     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20847-8_27     Document Type: Conference Paper

References (22)

1. Aha, D.W. (ed.): Lazy learning. Kluwer Academic Publishers, Dordrecht (1997)
2. Aha, D.W., et al.: Instance-based learning algorithms. Machine Learning 6 (1991)
3. Bosch, A., et al.: When small disjuncts abound, try lazy learning: A case study. In: BDCML (1997)
4. Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (1997)
5. Chawla, N.V., et al.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16 (2002)
6. Cover, T., Hart, P.: Nearest neighbor pattern classification. Institute of Electrical and Electronics Engineers Transactions on Information Theory 13 (1967)
7. Domingos, P.: Metacost: A general method for making classifiers cost-sensitive. In: KDD 1999 (1999)
8. Elkan, C.: The foundations of cost-sensitive learning. In: IJCAI (2001)
9. Fawcett, T., Provost, F.J.: Adaptive fraud detection. Data Mining and Knowledge Discovery 1(3) (1997)
10. Holte, R.C., et al.: Concept learning and the problem of small disjuncts. In: IJCAI 1989 (1989)
11. Kubat, M., et al.: Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30(2-3) (1998)
12. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: ICML 1997 (1997)
13. Ling, C., et al.: Data mining for direct marketing: Problems and solutions. In: KDD 1998 (1998)
14. Menzies, T., et al.: Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering 33 (2007)
15. Provost, F., et al.: The case against accuracy estimation for comparing induction algorithms. In: ICML 1998 (1998)
16. Provost, F.J., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42(3) (2001)
17. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
18. Swets, J.: Measuring the accuracy of diagnostic systems. Science 240(4857) (1988)
19. Ting, K.: The problem of small disjuncts: its remedy in decision trees. In: Canadian Conference on Artificial Intelligence (1994)
20. Weiss, G.M.: Mining with rarity: a unifying framework. SIGKDD Explorations 6(1) (2004)
21. Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning (2000)
22. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)