Abstract
The extreme learning machine model for ordinal classification is extended to the uncertain case. Dealing with epistemic uncertainty by Dempster-Shafer theory, in this paper, the single-model multi-output extreme learning machine is learned from evidential training data. Taking both the uncertainty and the ordering relation of labels into consideration, given mass functions of training labels, different evidential encoding schemes for model output are proposed. On that basis, adopting the structure of a single extreme learning machine model with multiple output nodes, the construction procedure of evidential ordinal classification model is designed. According to the encoding mechanism and learning details, when there is no epistemic uncertainty in training labels, the proposed evidential ordinal method can be reduced to the traditional ordinal one. Experiments on artificial and UCI datasets illustrate the practical implementation and effectiveness of proposed evidential extreme learning machine for ordinal classification.
Supported by the Natural Science Foundation of Shandong Province ZR2021MF074 and the National Key R & D Program of China 2018AAA0101703
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
Gutierrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., et al.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2016)
Xiao, Y., Li, X., Liu, B., et al.: Multi-view support vector ordinal regression with data uncertainty. Inf. Sci. 589, 516–530 (2022)
Lucas, K., Lisa, H., Torsten, H., et al.: Deep and interpretable regression models for ordinal outcomes. Pattern Recogn. 122, 108263 (2022)
Destercke, S.: On the median in imprecise ordinal problems. Ann. Oper. Res. 256(2), 375–392 (2017)
Destercke, S., Yang, G.: Cautious ordinal classification by binary decomposition. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 323–337. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_21
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol. 219, pp. 57–72. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-44792-4_3
Shafer, V.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
He, Y.: An approach utilizing negation of extended-dimensional vector of disposing mass for ordinal evidences combination in a fuzzy environment. arXiv preprint arXiv:2104.05416 (2021)
He, Y.: Ordinal relative belief entropy. arXiv preprint arXiv:2102.12575 (2021)
Campagner, A., Ciucci, D., Denœux, T.: Belief functions and rough sets: survey and new insights. Int. J. Approximate Reasoning 143, 192–215 (2022)
Smets, P.: Decision making in the TBM: the necessity of the pignistic transformation. Int. J. Approximate Reasoning 38(2), 133–147 (2005)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Deng, W.Y., Zheng, Q.H., Lian, S., et al.: Ordinal extreme learning machine. Neurocomputing 74(1–3), 447–456 (2010)
Ma, L., Denoeux, T.: Partial classification in the belief function framework. Knowl.-Based Syst. 214, 106742 (2021)
Dua, D., Graff, C.: UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science (2019). http://archive.ics.uci.edu/ml
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, L., Wei, P., Sun, B. (2022). Ordinal Classification Using Single-Model Evidential Extreme Learning Machine. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-17801-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17800-9
Online ISBN: 978-3-031-17801-6
eBook Packages: Computer ScienceComputer Science (R0)