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- Title
Multiresolution hierarchical support vector machine for classification of large datasets.
- Authors
Alwajidi, Safaa; Yang, Li
- Abstract
Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in comparison with existing SVM algorithms.
- Subjects
SUPPORT vector machines; MACHINE learning; SUPERVISED learning
- Publication
Knowledge & Information Systems, 2022, Vol 64, Issue 12, p3447
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-022-01755-9