Summary of Gb-rvfl: Fusion Of Randomized Neural Network and Granular Ball Computing, by M. Sajid et al.
GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing
by M. Sajid, A. Quadir, M. Tanveer
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, it treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need for inverting the entire training matrix. To address these issues, this paper proposes granular ball RVFL (GB-RVFL) and graph embedding GB-RVFL (GE-GB-RVFL) models, which use granular balls as inputs instead of training samples, enhancing scalability and improving robustness against noise and outliers. The proposed models are evaluated on KEEL, UCI, NDC, and biomedical datasets, demonstrating superior performance compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The random vector functional link (RVFL) network is a great tool for classifying things, but it has some weaknesses. It treats all the data equally, which isn’t always helpful. For example, sometimes you want to ignore some of the noisy or incorrect data points. The RVFL also gets slower and less accurate as you add more data. To fix these problems, researchers came up with two new models: granular ball RVFL (GB-RVFL) and graph embedding GB-RVFL (GE-GB-RVFL). These models use a different way of looking at the data that helps them be better at ignoring noisy data and handling big datasets. |
Keywords
» Artificial intelligence » Classification » Embedding » Generalization
