Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
<|reference_start|>Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method): 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.<|reference_end|>
@article{morris2024pre-sorted,
    title={Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)},
    author={Jordan Morris},
    journal={arXiv preprint arXiv:2403.09680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09680},
    primaryClass={cs.NE cs.AI cs.LG}
}