| Title | Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lundberg, Lars, Lennerstad, Håkan, Boeva, Veselka, García-Martín, Eva |
| Conference Name | Proceedings of the 2019 11th International Conference on Machine Learning and Computing |
| Publisher | Association for Computing Machinery |
| Conference Location | Zhuhai, China |
| ISBN Number | 978-1-4503-6600-7 |
| Keywords | composability, hyperplane folding, hyperplane hinging, non-linear relations, piecewise linear classification, Predictive Metrics, pubcrawl, Resiliency, Support vector machines |
| Abstract | 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. |
| URL | https://doi.org/10.1145/3318299.3318319 |
| DOI | 10.1145/3318299.3318319 |
| Citation Key | lundberg_handling_2019 |
Handling Non-linear Relations in Support Vector Machines through Hyperplane Folding