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.
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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.
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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
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