Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866907858601246720 |
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| author | Morris, Jordan |
| author_facet | Morris, Jordan |
| contents | This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, results demonstrate up to 10% improvement in accuracy, approx. 383X reduction in training time and approx. 86X reduction in inference time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method) Morris, Jordan Neural and Evolutionary Computing Artificial Intelligence Machine Learning B.6.0; B.7.0; C.1.0; I.2.6 This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, results demonstrate up to 10% improvement in accuracy, approx. 383X reduction in training time and approx. 86X reduction in inference time. |
| title | Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method) |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning B.6.0; B.7.0; C.1.0; I.2.6 |
| url | https://arxiv.org/abs/2403.09680 |