Margin-based Refinement for Support-Vector-Machine Classification

Helene Dörksen, Volker Lohweg


In real-world scenarios it is not always possible to generate an appropriate number of measured objects for machine learning tasks. At the learning stage, for small/incomplete datasets it is nonetheless often possible to get high accuracies for several arbitrarily chosen classifiers. The fact is that many classifiers might perform accurately, but decision boundaries might be inadequate. In this situation, the decision supported by marginlike characteristics for the discrimination of classes might be taken into account. Accuracy as an exclusive measure is often not sufficient. To contribute to the solution of this problem, we present a margin-based approach originated from an existing refinement procedure. In our method, margin value is considered as optimisation criterion for the refinement of SVM models. The performance of the approach is evaluated on a real-world application dataset for Motor Drive Diagnosis coming from the field of intelligent autonomous systems in the context of Industry 4.0 paradigm as well as on several UCI Repository samples with different numbers of features and objects.


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Paper Citation

in Harvard Style

Dörksen H. and Lohweg V. (2017). Margin-based Refinement for Support-Vector-Machine Classification . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 293-300. DOI: 10.5220/0006115502930300

in Bibtex Style

author={Helene Dörksen and Volker Lohweg},
title={Margin-based Refinement for Support-Vector-Machine Classification},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Margin-based Refinement for Support-Vector-Machine Classification
SN - 978-989-758-222-6
AU - Dörksen H.
AU - Lohweg V.
PY - 2017
SP - 293
EP - 300
DO - 10.5220/0006115502930300