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Handling non-linear relations in support vector machines through hyperplane folding
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
2019 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2019, p. 137-141Conference paper, Published paper (Refereed)
Abstract [en]

We present a new method, called hyperplane folding, that increases the margin in Support Vector Machines (SVMs). Based on the location of the support vectors, the method splits the dataset into two parts, rotates one part of the dataset and then merges the two parts again. This procedure increases the margin as long as the margin is smaller than half of the shortest distance between any pair of data points from the two different classes. We provide an algorithm for the general case with n-dimensional data points. A small experiment with three folding iterations on 3-dimensional data points with non-linear relations shows that the margin does indeed increase and that the accuracy improves with a larger margin. The method can use any standard SVM implementation plus some basic manipulation of the data points, i.e., splitting, rotating and merging. Hyperplane folding also increases the interpretability of the data. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2019. p. 137-141
Keywords [en]
Hyperplane folding, Hyperplane hinging, Non-linear relations, Piecewise linear classification, Support vector machines, Geometry, Piecewise linear techniques, Vectors, Different class, Interpretability, Nonlinear relations, Piecewise linear, Support vector, Support vector machine (SVMs)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18039DOI: 10.1145/3318299.3318319ISI: 000477981500023Scopus ID: 2-s2.0-85066460409OAI: oai:DiVA.org:bth-18039DiVA, id: diva2:1325037
Conference
11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-09-10Bibliographically approved

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Lundberg, LarsLennerstad, HåkanBoeva, VeselkaGarcía Martín, Eva

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
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Output format
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