Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding
This is an incremental application of machine learning to number theory, potentially useful for researchers in mathematics or computational fields.
The paper tackles prime number classification using a sparse encoding method with neural networks, achieving over 99% recall for primes and 79% for non-primes on test data of 2 million integers, with rapid convergence within one training epoch.
This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99\% in identifying prime numbers and 79\% for non-prime numbers from an inherently imbalanced sequential series of integers, while exhibiting rapid model convergence before the completion of a single training epoch. We performed training using $10^6$ integers starting from a specified integer and tested on a different range of $2 \times 10^6$ integers extending from $10^6$ to $3 \times 10^6$, offset by the same starting integer. While constrained by the memory capacity of our resources, which limited our analysis to a span of $3\times10^6$, we believe that our study contribute to the application of machine learning in prime number analysis. This work aims to demonstrate the potential of such applications and hopes to inspire further exploration and possibilities in diverse fields.