Manuscript received November 15, 2024; revised December 10, 2024; accepted December 20, 2024; published January 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
Cite: Bokyung Amy Kwon, "Instance Selection for MI-Support Vector Machines," International Journal of Machine Learning vol. 15, no. 1, pp. 13-16, 2025.
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).