doi: 10.18178/ijml.2024.15.1.1172
Instance Selection for MI-Support Vector Machines
Bokyung Amy Kwon*
- Department of Artificial Intelligence, College of Computing, Hanyang Univeristy, ERICA, 15588, Korea
- Manuscript receivedNovember 15, 2024
- revisedDecember 10, 2024
- acceptedDecember 20, 2024
- publishedJanuary 21, 2025
Abstract
Support Vector Machines (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in Multiple-Instance (MI) problems. Our study proposes a schematic algorithm for selecting instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under MI setting. We confirmed that SVMs in MI settings when using this instance selection strategy outperformed original approaches based on experiments with five benchmark datasets. In addition, Task Execution Times (TETs) were reduced by more than 80% based on MissSVM. Therefore, it is noteworthy to consider this representation adaptation for SVMs in MI setting.
Keywords
- support vector machine
- Margin
- Hausdorff distance
- representation selection
- multiple-instance learning
- machine learning
How to Cite
Bokyung Amy Kwon, "Instance Selection for MI-Support Vector Machines," International Journal of Machine Learning, vol. 15, no. 1, pp. 13-16, 2025. https://doi.org/10.18178/ijml.2024.15.1.1172
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).