Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
This is an incremental improvement for machine learning efficiency in classification tasks.
The paper tackles improving Tsetlin Machines by introducing a pre-sort stage using a genetic algorithm for K-Medoid clustering and alignment, resulting in up to 10% accuracy improvement, 383x faster training, and 86x faster inference on MNIST classification.
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.