Summary of Enhancing Robustness and Efficiency Of Least Square Twin Svm Via Granular Computing, by M. Tanveer et al.
Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing
by M. Tanveer, R. K. Sharma, A. Quadir, M. Sajid
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In machine learning, Least Square Twin Support Vector Machine (LSTSVM) is a top-performing model. However, it has limitations when dealing with noisy data, overlooking the Stable Recovery Model (SRM) principle and instability in resampling. Additionally, its computational complexity and reliance on matrix inversions hinder efficient processing of large datasets. To address these challenges, we propose Robust Granular Ball LSTSVM (GBLSTSVM), which uses granular balls instead of original data points to improve robustness. We also introduce Large-Scale GBLSTSVM (LS-GBLSTSVM) with the SRM principle through regularization terms for improved scalability and efficiency. Our proposed models outperform baseline models on UCI, KEEL, and NDC benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve machine learning models called LSTSVM. Current models can be affected by noisy data, which makes them less accurate. The authors propose a new model that uses “granular balls” instead of original data points, making it more robust and efficient. They also introduce a large-scale version of this model that works better with big datasets. Experiments show that these new models work better than existing ones on different datasets. |
Keywords
» Artificial intelligence » Machine learning » Regularization » Support vector machine
