Imbalanced classification problems: Systematic study, issues and best practices

Lecture Notes in Business Information Processing

Volumn 102 LNBIP, Issue , 2012, Pages 35-50

  Lemnaru, Camelia ^a   Potolea, Rodica ^a  

^a TECHNICAL UNIVERSITY OF CLUJ NAPOCA   (Romania)

Author keywords

Class imbalance; Classifiers; Comprehensive study; Metrics

Indexed keywords

ALGORITHMS; BENCHMARKING; CLASSIFIERS;

BENCHMARK DATA; BEST FIT; CLASS IMBALANCE; CLASS IMBALANCE PROBLEMS; CLASSIFICATION ALGORITHM; COMPREHENSIVE STUDIES; IMBALANCED CLASSIFICATION; METRICS; OVERALL VISION; POSSIBLE SOLUTIONS; SYSTEMATIC STUDY;

INFORMATION SYSTEMS;

EID: 84861676229     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-29958-2_3     Document Type: Conference Paper

References (22)

1. Barandela, R., Sanchez, J.S., Garcia, V., Rangel, E.: Strategies for Learning in Class Imbalance Problems. Pattern Recognition 36(3), 849-851 (2003)
2. Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis Journal 6, 429-449 (2002)
3. Weiss, G.: Mining with Rarity: A Unifying Framework. SIGKDD Explorations 6(1), 7-19 (2004)
4. Chawla, N.V.: Data Mining from Imbalanced Data Sets. In: Data Mining and Knowledge Discovery Handbook, ch. 40, pp. 853-867. Springer US (2006)
5. Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C., Kegelmeyer, P.: Comparative Evaluation of Pattern Recognition Techniques for Detection of Microcalcifications in Mammography. Int. Journal of Pattern Rec. and AI 7(6), 1417-1436 (1993)
6. Garcia, S., Herrera, F.: Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy. Evolutionary Computation 17(3), 275-306 (2009)
7. Batista, G.E.A.P.A., Prati, R.C. Monard, M.C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, 20-29 (2004)
8. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research 16, 321-357 (2002)
9. Guo, H., Viktor, H.L.: Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. Sigkdd Explorations 6, 30-39 (2004)
10. Brodersen, K.H., Ong, C.S., Stephen, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: Proceedings of the 20th Int. Conf. on Pattern Recognition, pp. 3121-3124 (2010)
11. Grzymala-Busse, J.W., Stefanowski, J., Wilk, S.: A Comparison of Two Approaches to Data Mining from Imbalanced Data. Journal of Intelligent Manufacturing 16, 65-573 (2005)
12. UCI Machine Learning Data Repository, http://archive.ics.uci.edu/ml/
13. Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann (2005)
14. Visa, S., Ralescu, A.: Issues in mining imbalanced data sets -a review paper. In: Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference, pp. 67-73 (2005)
15. Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: special issue on learning from I imbalanced data sets. SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets 6(1), 1-6 (2004)
16. Hall, L.O., Joshi, A.: Building Accurate Classifiers from Imbalanced Data Sets. In: IMACS 2005, Paris (2005)
17. Weiss, G., Provost, F.: Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research 19, 315-354 (2003)
18. Chan, P., Stolfo, S.: Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 164-168. AAAI Press, Menlo Park (1998)
19. Vidrighin Bratu, C., Muresan, T., Potolea, R.: Improving Classification Accuracy through Feature Selection. In: Proceedings of the 4th IEEE International Conference on Intelligent Computer Communication and Processing, ICCP 2008, pp. 25-32 (2008)
20. Provost, F.: Learning with Imbalanced Data Sets. Invited paper for AAAI 2000 Workshop on Imbalanced Data Sets (2000)
21. Joshi, M.V., Agarwal, R.C., Kumar, V.: Predicting rare classes: can boosting make any weak learner strong? In: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining, pp. 297-306 (2002)
22. Weiss, G.M., Hirsh, H.: A quantitative study of small disjuncts. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 665-670. AAAI Press (2000)