Contextualizing Support Vector Machine Predictions

Authors

Marcelo Loor^{1, 2, *,} , Guy De Tré^1,

¹Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41 B-9000, Ghent, 9000, Belgium

²Department of Electrical and Computer Engineering, ESPOL Polytechnic University, Campus Gustavo Galindo V., Km. 30.5 Via Perimetral, Guayaquil, 09015863, Ecuador

^*Corresponding author. Email: Marcelo.Loor@UGent.be

DOI

10.2991/ijcis.d.200910.002How to use a DOI?

Keywords

Explainable artificial intelligence; Augmented appraisal degrees; Context handling; Support vector machine classification

Abstract

Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context. A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Marcelo Loor
AU  - Guy De Tré
PY  - 2020
DA  - 2020/09/22
TI  - Contextualizing Support Vector Machine Predictions
JO  - International Journal of Computational Intelligence Systems
SP  - 1483
EP  - 1497
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200910.002
DO  - 10.2991/ijcis.d.200910.002
ID  - Loor2020
ER  -