A pruning-based approach for searching precise and generalized region for synthetic minority over-sampling

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

Volumn 7301 LNAI, Issue PART 2, 2012, Pages 371-382

  Puntumapon, Kamthorn ^a   Waiyamai, Kitsana ^a  

^a KASETSART UNIVERSITY   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS DISTRIBUTIONS; CLASS IMBALANCE; DECISION REGIONS; F-MEASURE; MULTIPLE SET; NEAREST NEIGHBORS; OVER SAMPLING; PERFORMANCE IMPROVEMENTS; SIGNIFICANT IMPACTS; SYNTHETIC DATA; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES;

LEARNING ALGORITHMS;

DATA MINING;

EID: 84861433287     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30220-6_31     Document Type: Conference Paper

References (14)

1. Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145-1159 (1997)
2. Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 475-482. Springer, Heidelberg (2009), http://dblp.uni-trier.de/db/conf/pakdd/pakdd2009.html
3. 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)
4. Fan, X., Tang, K., Weise, T.: Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 309-320. Springer, Heidelberg (2011)
5. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
6. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1), 10-18 (2009)
7. Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005, Part I. LNCS, vol. 3644, pp. 878-887. Springer, Heidelberg (2005)
8. He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: IJCNN, pp. 1322-1328. IEEE (2008), http://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2008.html
9. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21, 1263-1284 (2009)
10. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
11. Ramsey, P.H., Hodges, J.L., Shaffer, J.P.: Significance probabilities of the wilcoxon signe-drank test. Journal of Nonparametric Statistics 2(2), 133-153 (1993), http://www.informaworld.com/10.1080/10485259308832548
12. van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)
13. Weiss, G.M.: Mining with rarity: a unifying framework. SIGKDD Explorations 6(1), 7-19 (2004), http://dblp.uni-trier.de/db/journals/sigkdd/ sigkdd6.html
14. Yang, Q., Wu, X.: 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making 5(4), 597-604 (2006), http://dblp.uni-trier.de/db/journals/ijitdm/ijitdm5.html