Chance constrained conic-segmentation support vector machine with uncertain data
This addresses classification robustness for uncertain data in machine learning, but it is incremental as it builds on existing CS-SVM methods.
The paper tackles the problem of multiclass classification with uncertain or mislabeled data by extending conic-segmentation SVM to a chance-constrained approach, and experimental results show it ensures a small probability of misclassification.
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.