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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

8. Imbalance in Multilabel Datasets

verfasst von : Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus

Erschienen in: Multilabel Classification

Verlag: Springer International Publishing

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Abstract

The frequency of class labels in many datasets is not even. On the contrary, that a certain class appears in a large portion of the data samples while other is scarcely represented is something quite usual. This casuistic produces a problem generically labeled as class imbalance. Due to these differences between class distributions, a specific need arises, imbalanced learning. This chapter beings introducing the mentioned task in Sect. 8.1. Then, the specific aspects of imbalance in the multilabel area are discussed in Sect. 8.2. Section 8.3 explains how imbalance in MLC has been faced, enumerating a considerable set of proposals. Some of them are experimentally evaluated in Sect. 8.4. Lastly, Sect. 8.5 summarizes the contents.

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Vorheriges Kapitel Dimensionality Reduction
Nächstes Kapitel Multilabel Software
Fußnoten
1
The frequency (Y-axis) scale is individually adjusted to show better the relevance of labels in each MLD, instead of being common to all plots.
 
2
These plots were generated by the mldr R package, described in the following chapter.
 
3
The implementations of these methods can be found in the links section provided in this book repository [7], along with dataset partitions.
 
Literatur
1.
Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: Resampling multilabel datasets by decoupling highly imbalanced labels. In: Proceedings of 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS’15, vol. 9121, pp. 489–501. Springer (2015)
2.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: A first approach to deal with imbalance in multi-label datasets. In: Proceedings of 8th International Conference on Hybrid Artificial Intelligent Systems, HAIS’13, vol. 8073, pp. 150–160. Springer (2013)
3.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Concurrence among imbalanced labels and its influence on multilabel resampling algorithms. In: Proceedings of 9th International Conference on Hybrid Artificial Intelligent Systems, HAIS’14, vol. 8480. Springer (2014)
4.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: MLeNN: a first approach to heuristic multilabel undersampling. In: Proceedings of 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL’14, vol. 8669, pp. 1–9. Springer (2014)
5.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163, 3–16 (2015)CrossRef
6.
Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: MLSMOTE: approaching imbalanced multilabel learning through synthetic instance generation. Knowl. Based Syst. 89, 385–397 (2015)CrossRef
7.
8.
Chen, K., Lu, B., Kwok, J.: Efficient classification of multi-label and imbalanced data using min-max modular classifiers. In: Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN’06, pp. 1770–1775 (2006)
9.
Dendamrongvit, S., Kubat, M.: Undersampling approach for imbalanced training sets and induction from multi-label text-categorization domains. In: New Frontiers in Applied Data Mining. LNCS, vol. 5669, pp. 40–52. Springer (2010)
10.
Fernández, A., López, V., Galar, M., del Jesus, M.J., Herrera, F.: Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches. Knowl. Based Syst. 42, 97–110 (2013)CrossRef
11.
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. pattern Recogn. 44(8), 1761–1776 (2011)CrossRef
12.
Giraldo-Forero, A.F., Jaramillo-Garzón, J.A., Ruiz-Muñoz, J.F., Castellanos-Domínguez, C.G.: Managing imbalanced data sets in multi-label problems: a case study with the SMOTE algorithm. In: Proceedings of 18th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP’13, vol. 8258, pp. 334–342. Springer (2013)
13.
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRef
14.
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)
15.
Li, C., Shi, G.: Improvement of learning algorithm for the multi-instance multi-label RBF neural networks trained with imbalanced samples. J. Inf. Sci. Eng. 29(4), 765–776 (2013)
16.
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)CrossRef
17.
Lu, B., Ito, M.: Task decomposition and module combination based on class relations: a modular neural network for pattern classification. IEEE Trans. Neural Netw. 10(5), 1244–1256 (1999)CrossRef
18.
Nitesh, V.C., Kevin, W.B., Lawrence, O.H., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)MATH
19.
Prati, R.C., Batista, G.E., Silva, D.F.: Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowl. Inf. Syst. 45(1), 247–270 (2015)CrossRef
20.
Quinlan, J.R.: C4.5: Programs for Machine Learning (1993)
21.
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall (2003)
22.
Sun, Y., Wong, A.K.C., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recogn. Artif. Intell. 23(4), 687–719 (2009)CrossRef
23.
Tahir, M.A., Kittler, J., Bouridane, A.: Multilabel classification using heterogeneous ensemble of multi-label classifiers. Pattern Recogn. Lett. 33(5), 513–523 (2012)CrossRef
24.
Tahir, M.A., Kittler, J., Yan, F.: Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recogn. 45(10), 3738–3750 (2012)CrossRef
25.
Tepvorachai, G., Papachristou, C.: Multi-label imbalanced data enrichment process in neural net classifier training. In: Proceedings of IEEE International Joint Conference on Neural Networks, IJCNN’08, pp. 1301–1307. IEEE (2008)
26.
Zhang, M., Wang, Z.: MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing 72(16), 3951–3956 (2009)CrossRef
Metadaten
Titel
Imbalance in Multilabel Datasets
verfasst von
Francisco Herrera
Francisco Charte
Antonio J. Rivera
María J. del Jesus
Copyright-Jahr
2016
Buch
Multilabel Classification

Print ISBN: 978-3-319-41110-1

Electronic ISBN: 978-3-319-41111-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-41111-8_8

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