Chance constrained conic-segmentation support vector machine with uncertain data

• URL: http://arxiv.org/abs/2107.13319v1
• Date: Wed, 28 Jul 2021 12:29:47 GMT
• Title: Chance constrained conic-segmentation support vector machine with uncertain data
• Authors: Shen Peng, Gianpiero Canessa
• Abstract summary: Support vector machines (SVM) is one of the well known supervised classes of learning algorithms. This paper studies CS-SVM when the data points are uncertain or mislabelled.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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