Abstract
Finding suitable mechanisms whereby rationale behind support vector machine (SVM) predictions can be known and understood without substantial difficulties is an ongoing challenge. Aiming to find such a mechanism, we look into the contextualization of SVM models. Hence, we propose a novel explainable SVM classifier that makes use of a parallel arrangement of contextualized SVM models for offering predictions that depend on a particular event, situation or idea. The proposed classifier allows decision makers to state in a clear manner the context of the predictions they would like to be offered. This aspect is deemed to be important since decision makers can take advantage of the improvement in the interpretability of such contextualized predictions for making more informed decisions. The improvement in interpretability is illustrated through an example in which digitized handwritten vowels are contextually identified. Another example where hand gestures are recognized by means of electromyography (EMG) signals shows how the proposed classifier can also improve the accuracy of the resulting models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986). https://doi.org/10.1016/S0165-0114(86)80034-3
Barakat, N.H., Bradley, A.P.: Rule extraction from support vector machines: a review. Neurocomputing 74(1), 178–190 (2010). https://doi.org/10.1016/j.neucom.2010.02.016
Barredo Arrieta, A., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012
Bovolo, F., Bruzzone, L., Marconcini, M.: A novel context-sensitive SVM for classification of remote sensing images. In: 2006 IEEE International Symposium on Geoscience and Remote Sensing, pp. 2498–2501 (2006). https://doi.org/10.1109/IGARSS.2006.646
Bruzzone, L., Persello, C.: A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples. IEEE Trans. Geosci. Remote Sens. 47(7), 2142–2154 (2009). https://doi.org/10.1109/TGRS.2008.2011983
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998). https://doi.org/10.1023/A:1009715923555
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504
García-Gutiérrez, J., Mateos-García, D., Garcia, M., Riquelme-Santos, J.C.: An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion. Neurocomputing 163, 17–24 (2015). https://doi.org/10.1016/j.neucom.2014.08.086
Graf, H., Cosatto, E., Bottou, L., Dourdanovic, I., Vapnik, V.: Parallel support vector machines: the cascade SVM. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17. MIT Press (2004). https://proceedings.neurips.cc/paper/2004/file/d756d3d2b9dac72449a6a6926534558a-Paper.pdf
Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F.: Factual and counterfactual explanations for black box decision making. IEEE Intell. Syst. 34(6), 14–23 (2019). https://doi.org/10.1109/MIS.2019.2957223
Kaminski, M.E.: The right to explanation, explained. Berkeley Technol. Law J. 34, 189 (2019). https://doi.org/10.15779/Z38TD9N83H
Lengua, M.A.C., Quiroz, E.A.P.: A systematic literature review on support vector machines applied to classification. In: 2020 IEEE Engineering International Research Conference (EIRCON), pp. 1–4. IEEE (2020)
Lobov, S., Krilova, N., Kastalskiy, I., Kazantsev, V., Makarov, V.: EMG data for gestures data set - UCI machine learning repository (2019). https://archive.ics.uci.edu/ml/datasets/EMG+data+for+gestures
Loor, M., De Tré, G.: On the need for augmented appraisal degrees to handle experience-based evaluations. Appl. Soft Comput. 54, 284–295 (2017). https://doi.org/10.1016/j.asoc.2017.01.009
Loor, M., De Tré, G.: Identifying and properly handling context in crowdsourcing. Appl. Soft Comput. 73, 203–214 (2018). https://doi.org/10.1016/j.asoc.2018.04.062
Loor, M., De Tré, G.: Explaining computer predictions with augmented appraisal degrees. In: 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Atlantis Press (2019). https://doi.org/10.2991/eusflat-19.2019.24
Loor, M., De Tré, G.: Contextualizing support vector machine predictions. Int. J. Comput. Intell. Syst. 13, 1483–1497 (2020). https://doi.org/10.2991/ijcis.d.200910.002
Loor, M., De Tré, G.: Handling subjective information through augmented (fuzzy) computation. Fuzzy Sets Syst. 391, 47–71 (2020). https://doi.org/10.1016/j.fss.2019.05.007
Loor, M., Tapia-Rosero, A., De Tré, G.: An open-source software library for explainable support vector machine classification. In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2022). https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882731
Malowany, D., Guterman, H.: Biologically inspired visual system architecture for object recognition in autonomous systems. Algorithms 13(7), 167 (2020)
Martens, D., Baesens, B., Van Gestel, T.: Decompositional rule extraction from support vector machines by active learning. IEEE Trans. Knowl. Data Eng. 21(2), 178–191 (2009). https://doi.org/10.1109/TKDE.2008.131
Migliorini, S., Belussi, A., Quintarelli, E., Carra, D.: A context-based approach for partitioning big data. In: EDBT, pp. 431–434 (2020)
Negri, R.G., Dutra, L.V., Sant’Anna, S.J.S.: An innovative support vector machine based method for contextual image classification. ISPRS J. Photogramm. Remote. Sens. 87, 241–248 (2014). https://doi.org/10.1016/j.isprsjprs.2013.11.004
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1135–1144. ACM, New York (2016). https://doi.org/10.1145/2939672.2939778
Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X
Zhu, P., Hu, Q.: Rule extraction from support vector machines based on consistent region covering reduction. Knowl.-Based Syst. 42, 1–8 (2013). https://doi.org/10.1016/j.knosys.2012.12.003
Acknowledgements
This study has been supported by both the research project “Interpretable Artificial Intelligence (XAI) in Group Decision-Making” (FIEC-200-2020) from ESPOL Polytechnic University and the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” from the Flemish Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Loor, M., Tapia-Rosero, A., De Tré, G. (2023). Contextual Boosting to Explainable SVM Classification. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-39965-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-39964-0
Online ISBN: 978-3-031-39965-7
eBook Packages: Computer ScienceComputer Science (R0)