Abstract
Medical data mining problems are usually characterized by examples of some of the classes appearing more frequently. Such a learning difficulty is known as imbalanced classification problems. This contribution analyzes the application of algorithms for tackling multi-class imbalanced classification in the field of vertebral column diseases classification. Particularly, we study the effectiveness of applying a recent approach, known as Selective Oversampling for Multi-class Imbalanced Datasets (SOMCID), which is based on analyzing the structure of the classes to detect those examples in minority classes that are more interesting to oversample. Even though SOMCID has been previously applied to data belonging to different domains, its suitability in the difficult vertebral column medical data has not been analyzed until now. The results obtained show that the application of SOMCID for the detection of pathologies in the vertebral column may lead to a significant improvement over state-of-the-art approaches that do not consider the importance of the types of examples.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Berthonnaud, E., Dimnet, J., Roussouly, P., Labelle, H.: Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters. J. Spinal Disord. Tech. 18(1), 40–47 (2005)
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)
Davies, E.: Training sets and a priori probabilities with the nearest neighbour method of pattern recognition. Pattern Recognit. Lett. 8(1), 11–13 (1988)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Fernández-Navarro, F., Hervás-Martínez, C., Gutiérrez, P.A.: A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recognit. 44(8), 1821–1833 (2011)
Ferri, C., Hernández, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recognit. Lett. 30(1), 27–38 (2009)
Kang, Q., Chen, X., Li, S., Zhou, M.: A noise-filtered under-sampling scheme for imbalanced classification. IEEE Trans. Cybern. 47(12), 4263–4274 (2017)
Krawczyk, B., Schaefer, G.: A hybrid classifier committee for analysing asymmetry features in breast thermograms. Appl. Soft Comput. 20, 112–118 (2014)
Krawczyk, B., Woźniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Inf. Sci. 264, 182–195 (2014)
Krawczyk, B., Woźniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554–562 (2014)
Krawczyk, B., Filipczuk, P.: Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Eng. Appl. Artif. Intell. 31, 126–135 (2014)
Li, J., Fong, S., Wong, R.K., Chu, V.W.: Adaptive multi-objective swarm fusion for imbalanced data classification. Inf. Fusion 39, 1–24 (2018)
Menardi, G., Torelli, N.: Training and assessing classification rules with imbalanced data. Data Min. Knowl. Discov. 28(1), 92–122 (2014)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
da Rocha Neto, A.R., Sousa, R., de A. Barreto, G., Cardoso, J.S.: Diagnostic of pathology on the vertebral column with embedded reject option. In: Vitriá, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 588–595. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_73
Sáez, J.A., Krawczyk, B., Wozniak, M.: Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. Pattern Recognit. 57, 164–178 (2016)
Sardari, S., Eftekhari, M., Afsari, F.: Hesitant fuzzy decision tree approach for highly imbalanced data classification. Appl. Soft Comput. 61, 727–741 (2017)
Wang, S., Yao, X.: Multiclass imbalance problems: analysis and potential solutions. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(4), 1119–1130 (2012)
Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res. 6(1), 1–34 (1997)
Zhou, Z., Liu, X.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)
Acknowledgment
José A. Sáez holds a Juan de la Cierva-formación fellowship (Ref. FJCI-2015-25547) from the Spanish Ministry of Economy, Industry and Competitiveness. Bartosz Krawczyk and Michał Woźniak are partially supported by the Polish National Science Center under the grant no. UMO-2015/19/B/ST6/01597.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sáez, J.A., Quintián, H., Krawczyk, B., Woźniak, M., Corchado, E. (2018). Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)