Periodic Linear Kernel for Cancer Classification Datasets
Mohamed Oubraime, Rida El Abassi, Abderrahim Sabour · 2024
In this study, we introduced and tested a new SVM kernel, which we termed the “periodic linear kernel.” The results obtained using evaluation metrics such as accuracy demonstrate that this kernel offers competitive performance compared to traditional kernels. While these findings are promising, they are based on a limited dataset. Additional research on larger and more diverse datasets is needed to confirm the generalizability of these results. These findings pave the way for future research on optimizing the periodic kernel and applying it to other types of medical data, with the goal of improving diagnostic tools and clinical decision-making. Furthermore, additional studies could expand its use to fields such as finance, image processing, and natural language processing, to address various classification and prediction challenges.