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arXiv · 2107.13319

Chance constrained conic-segmentation support vector machine with uncertain data

Shen Peng·Gianpiero Canessa·Zhihua Allen-Zhao
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Abstract

Support vector machines (SVM) is one of the well known supervised classes of learning algorithms. Furthermore, the conic-segmentation SVM (CS-SVM) is a natural multiclass analogue of the standard binary SVM, as CS-SVM models are dealing with the situation where the exact values of the data points are known. This paper studies CS-SVM when the data points are uncertain or mislabelled. With some properties known for the distributions, a chance-constrained CS-SVM approach is used to ensure the small probability of misclassification for the uncertain data. The geometric interpretation is presented to show how CS-SVM works. Finally, we present experimental results to investigate the chance constrained CS-SVM's performance.

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Shen Peng, Gianpiero Canessa, Zhihua Allen-Zhao. 2021-07-28. Chance constrained conic-segmentation support vector machine with uncertain data. https://arxiv.org/abs/2107.13319

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