GBSVM: Granular-ball Support Vector Machine
Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao, Jiancu Chen, Xiaoli, Peng

TL;DR
GBSVM introduces a novel classifier that uses granular-balls instead of individual data points, with corrected models and optimized algorithms, demonstrating robustness and efficiency on benchmark datasets.
Contribution
This paper corrects the errors in the original GBSVM model, derives its dual form, and develops faster algorithms for practical implementation.
Findings
Demonstrates GBSVM's robustness on benchmark datasets
Shows improved speed and stability with the designed algorithms
Provides open-source implementation for reproducibility
Abstract
GBSVM (Granular-ball Support Vector Machine) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular-ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, this paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. Furthermore, a particle swarm optimization algorithm is designed to solve the dual model. The sequential minimal optimization algorithm is also carefully designed to solve the dual model. The solution is faster and more stable than the particle swarm optimization based version. The experimental results on the UCI benchmark datasets demonstrate that GBSVM…
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Taxonomy
TopicsVehicle License Plate Recognition · Advanced Algorithms and Applications · Machine Learning and ELM
MethodsLib
