Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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Kernel Choice in One-Class Support Vector Machines for Novelty Detection

Authors
Qinong Tian1, *, Peixi Liu2, Tian Wu3, Ziyue Chen4
1School of Mathematics and Statistics, Xi’an Jiao Tong University, Xi’an, 710049, China
2Faculty of Science and Technology, University of Macao, Macao, 999078, China
3Department of Statistics, Cornell College, Mount Vernon, lowa, 52314, USA
4Kings College Alicante, 03016, Alicante, Spain
*Corresponding author. Email: tianqinong@stu.xjtu.edu.cn
Corresponding Author
Qinong Tian
Available Online 29 December 2022.
DOI
10.2991/978-94-6463-102-9_124How to use a DOI?
Keywords
Unsupervised Learning; Novelty Detection; One-Class Support Vector Machines; Kernel Methods; Hyperparameter Selection
Abstract

This work concentrates on novelty detection, a semi-supervised learning problem concerned with deciding if the new observation is sufficiently different from the ones seen so far. This paper mainly considers a variant of the support vector classification approach, which estimates the contours of the distribution of the initial observations and then can be used to decide if the new observations are abnormal. We try to estimate a negative function on the outlier points in the input space and a positive on the complement. A kernel expansion gives this decision function. The effectiveness of this kernel method is closely related to the choice of kernel functions and hyperparameters. Due to the demand for general and effective hyperparameter selection regulations, we investigate three approaches, including GridSearch in Python, median heuristic and Bayesian kernel learning. Some relevant experiments are performed in this paper. According to the experiments, we have learned that the choice of kernels and parameters can greatly influence the detection result.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
29 December 2022
ISBN
10.2991/978-94-6463-102-9_124
ISSN
2589-4900
DOI
10.2991/978-94-6463-102-9_124How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Qinong Tian
AU  - Peixi Liu
AU  - Tian Wu
AU  - Ziyue Chen
PY  - 2022
DA  - 2022/12/29
TI  - Kernel Choice in One-Class Support Vector Machines for Novelty Detection
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 1200
EP  - 1209
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_124
DO  - 10.2991/978-94-6463-102-9_124
ID  - Tian2022
ER  -