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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2409.02079 |
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| _version_ | 1866910948957093888 |
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| author | Williams, Alice Kovalerchuk, Boris |
| author_facet | Williams, Alice Kovalerchuk, Boris |
| contents | Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as interattribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates Visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02079 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates Williams, Alice Kovalerchuk, Boris Machine Learning Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as interattribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates Visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers. |
| title | Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.02079 |