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Handling non-linear relations in support vector machines through hyperplane folding
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3128-191x
2019 (engelsk)Inngår i: ACM International Conference Proceeding Series, Association for Computing Machinery , 2019, s. 137-141Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Association for Computing Machinery , 2019. s. 137-141
Emneord [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)
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Identifikatorer
URN: urn:nbn:se:bth-18039DOI: 10.1145/3318299.3318319ISI: 000477981500023Scopus ID: 2-s2.0-85066460409OAI: oai:DiVA.org:bth-18039DiVA, id: diva2:1325037
Konferanse
11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February
Tilgjengelig fra: 2019-06-14 Laget: 2019-06-14 Sist oppdatert: 2019-09-10bibliografisk kontrollert

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Lundberg, LarsLennerstad, HåkanBoeva, VeselkaGarcía Martín, Eva

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