Top

Published in:

2018 | OriginalPaper | Chapter

A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization

Authors : Maisa Daoud, Michael Mayo

Published in: AI 2018: Advances in Artificial Intelligence

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

A new synthetic minority class over-sampling approach for binary (normal/cancer) classification of microarray gene expression data is proposed. The idea is to exploit a previously trained autoencoder in combination with the Particle Swarm Optimisation algorithm to generate new synthetic examples of the minority class for solving the class imbalance problem. Experiments using two different autoencoder representation sizes (500 and 30) and two base classifiers (Support Vector Machine and naïve Bayes) show that the proposed method is able to generate discriminating representations that outperformed state-of-the-art methods such as Synthetic Minority Class Over-sampling Technique and Density-Based Synthetic Minority Class Over-sampling Technique in many test cases.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Stochastic Conjugate Gradient Descent Twin Support Vector Machine for Large Scale Pattern Classification

next chapter Co-evolution of Novel Tree-Like ANNs and Activation Functions: An Observational Study

https://portal.gdc.cancer.gosv/.

https://pythonhosted.org/pyswarm/.

Abadi, M. et. al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). https://www.tensorflow.org/

Sukarna, B., Md Monirul, I., Xin, Y., Kazyuki, M.: MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (2014)CrossRef

Blagus, R., Lusa, L.: Evaluation of smote for high-dimensional class-imbalanced microarray data. In: 11th International Conference on Machine Learning and Applications (icmla), vol. 2, pp. 89–94. IEEE (2012)

Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16CrossRef

Chawla, N., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRef

Antonio, C., et al.: TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. (2015). https://doi.org/10.1093/nar/gkv1507CrossRef

Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH

Dong, Y., Du, B., Zhang, L., Zhang, L.: Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning. IEEE Trans. Geosci. Remote Sens. 55(5), 2509–2524 (2017)CrossRef

Fawcett, T., Provost, F.: Adaptive fraud detection. Data Min. Knowl. Discov. 1(3), 291–316 (1997)CrossRef

10.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, H.I.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–8 (2009)CrossRef

11.

Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science, pp. 977–979. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2CrossRef

12.

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. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005). https://doi.org/10.1007/11538059_91CrossRef

13.

Japkowicz, N.: The class imbalance problem: significance and strategies. In: Proceedings of the International Conference on Artificial Intelligence (2005)

14.

Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML 1997, Nashville, USA (1997)

15.

Lin, W.J., Chen, J.J.: Class-imbalanced classifiers for high-dimensional data. Brief. Bioinform. 14(1), 13–26 (2012)CrossRef

16.

Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRef

17.

Siriseriwan, W.: Smotefamily: A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE (2016)

18.

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetMATH

19.

Weiss, G.M.: Mining with rarity: a unifying framework. ACM Sigkdd Explor. Newsl. 6(1), 7–19 (2014)CrossRef

20.

Zheng, Z., Wu, X., Srihari, R.: Feature selection for text categorization on imbalanced data. ACM Sigkdd Explor. Newsl. 6(1), 80–89 (2004)CrossRef

Title: A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization
Authors: Maisa Daoud
Michael Mayo
Publisher: Springer International Publishing
Book: AI 2018: Advances in Artificial Intelligence
Print ISBN: 978-3-030-03990-5

Electronic ISBN: 978-3-030-03991-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03991-2_55

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner