Abstract
Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Angelova, A., Abu-Mostafam, Y., Perona, P.: Pruning training sets for learning of object categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 494–501. IEEE (2005)
Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. J. Artif. Intell. Res. 11, 131–167 (1999)
Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017)
Folleco, A., Khoshgoftaar, T.M., Van Hulse, J., Bullard, L.: Identifying learners robust to low quality data. In: 2008 IEEE International Conference on Information Reuse and Integration, IRI 2008, pp. 190–195. IEEE (2008)
Frénay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845–869 (2014)
Gamberger, D., Lavrac, N., Groselj, C.: Experiments with noise filtering in a medical domain. In: ICML, pp. 143–151 (1999)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)
Jeatrakul, P., Wong, K.W., Fung, C.C.: Data cleaning for classification using misclassification analysis. J. Adv. Comput. Intell. Intell. Inf. 14(3), 297–302 (2010)
Khoshgoftaar, T.M., Rebours, P.: Generating multiple noise elimination filters with the ensemble-partitioning filter. In: 2004 Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, IRI 2004, pp. 369–375. IEEE (2004)
Khoshgoftaar, T.M., Rebours, P.: Improving software quality prediction by noise filtering techniques. J. Comput. Sci. Technol. 22(3), 387–396 (2007)
Miranda, A.L.B., Garcia, L.P.F., Carvalho, A.C.P.L.F., Lorena, A.C.: Use of classification algorithms in noise detection and elimination. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 417–424. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02319-4_50
Pang, G., Cao, L., Chen, L., Lian, D., Liu, H.: Sparse modeling-based sequential ensemble learning for effective outlier detection in high-dimensional numeric data (2018)
Pechenizkiy, M., Tsymbal, A., Puuronen, S., Pechenizkiy, O.: Class noise and supervised learning in medical domains: the effect of feature extraction. In: 2006 19th IEEE International Symposium on CBMS 2006 Computer-Based Medical Systems, pp. 708–713. IEEE (2006)
Sáez, J.A., Galar, M., Luengo, J., Herrera, F.: INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inf. Fusion 27, 19–32 (2016)
Sánchez, J.S., Pla, F., Ferri, F.J.: Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recogn. Lett. 18(6), 507–513 (1997)
Teng, C.M.: Dealing with data corruption in remote sensing. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 452–463. Springer, Heidelberg (2005). https://doi.org/10.1007/11552253_41
Thongkam, J., Xu, G., Zhang, Y., Huang, F.: Support vector machine for outlier detection in breast cancer survivability prediction. In: Ishikawa, Y., et al. (eds.) APWeb 2008. LNCS, vol. 4977, pp. 99–109. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89376-9_10
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 3, 408–421 (1972)
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38(3), 257–286 (2000)
Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 1–47 (2018)
Acknowledgements
This research was supported by Nature Science Foundation of China (Grant No. 61672284), Natural Science Foundation of Jiangsu Province (Grant No. BK20171418), China Postdoctoral Science Foundation (Grant No. 2016M591841), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C). This research was also supported by the Fundamental Research Funds for the Central Universities (No. NS2016089). Meanwhile, this research work was supported by Zayed University Research Cluster Award # R18038.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, K., Guan, D., Yuan, W., Li, B., Khattak, A.M., Alfandi, O. (2018). A Novel Feature Selection-Based Sequential Ensemble Learning Method for Class Noise Detection in High-Dimensional Data. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-05090-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05089-4
Online ISBN: 978-3-030-05090-0
eBook Packages: Computer ScienceComputer Science (R0)