A first approach to deal with imbalance in multi-label datasets

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

Volumn 8073 LNAI, Issue , 2013, Pages 150-160

  Charte, Francisco ^a   Rivera, Antonio ^b   Del Jesus, María José ^b   Herrera, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

Author keywords

Imbalanced Datasets; Measures; Multi label Classification; Preprocessing

Indexed keywords

IMBALANCED DATA-SETS; MEASURES; MULTI-CLASS CLASSIFICATION; MULTI-LABEL; MULTI-LABEL CLASSIFICATIONS; PRE-PROCESSING METHOD; PREPROCESSING; PROCESS OF LEARNING;

INTELLIGENT SYSTEMS;

CLASSIFICATION (OF INFORMATION);

EID: 84884913245     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40846-5_16     Document Type: Conference Paper

References (26)

1. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining Multi-label Data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, ch. 34, pp. 667-685. Springer US, Boston (2010)
2. Zhang, M.-L.: Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338-1351 (2006)
3. Wieczorkowska, A., Synak, P., Raś, Z.: Multi-Label Classification of Emotions in Music. In: Intel. Inf. Proces. and Web Mining, ch. 30, vol. 35, pp. 307-315 (2006)
4. Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl. 6(1), 1-6 (2004)
5. Tahir, M.A., Kittler, J., Bouridane, A.: Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recognit. Letters 33(5), 513-523 (2012)
6. Tahir, M.A., Kittler, J., Yan, F.: Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognit. 45(10), 3738-3750 (2012)
7. He, J., Gu, H., Liu,W.: Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites. PloS One 7(6), 7155 (2012)
8. Diplaris, S., Tsoumakas, G., Mitkas, P.A., Vlahavas, I.: Protein Classification with Multiple Algorithms. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 448-456. Springer, Heidelberg (2005)
9. Godbole, S., Sarawagi, S.: Discriminative Methods for Multi-Labeled Classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22-30. Springer, Heidelberg (2004)
10. Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artificial Intelligence 172(16), 1897-1916 (2008)
11. Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757-1771 (2004)
12. Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42-53. Springer, Heidelberg (2001) (Pubitemid 33331849)
13. Zhang, M., Zhou, Z.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038-2048 (2007)
14. Zhang, M.-L.: Ml-rbf: RBF Neural Networks for Multi-label Learning. Neural Process. Lett. 29, 61-74 (2009)
15. Elisseeff, A., Weston, J.: A Kernel Method for Multi-Labelled Classification. In: Adv. Neural Inf. Processing Systems 14, vol. 14, pp. 681-687. MIT Press (2001)
16. Japkowicz, N., Stephen, S.: The class imbalance problem: A systematic study. Intell. Data Anal. 6(5), 429-449 (2002)
17. Japkowicz, N.: Learning from imbalanced data sets: A comparison of various strategies, pp. 10-15. AAAI Press (2000)
18. Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42, 203-231 (2001)
19. Kotsiantis, S.B., Pintelas, P.E.: Mixture of expert agents for handling imbalanced data sets. Annals of Mathematics, Computing & Teleinformatics 1, 46-55 (2003)
20. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321-357 (2002)
21. Tsoumakas, G., Xioufis, E.S., Vilcek, J., Vlahavas, I.: MULAN multi-label dataset repository, http://mulan.sourceforge.net/datasets.html
22. Tsoumakas, G., Vlahavas, I.: Random k-labelsets: An ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406-417. Springer, Heidelberg (2007)
23. Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In: Proc. ECML/PKDD Workshop on Mining Multidimensional Data, pp. 30-44 (2008)
24. Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 145-158. Springer, Heidelberg (2011)
25. Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73, 133-153 (2008)
26. Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2-3), 211-225 (2009)