Comparison of fuzzy clustering based SVM with reinforcement learning based SVM for autocoding of the Family Income and Expenditure Survey
Yukako Toko, Mika Sato‐Ilic · Procedia Computer Science · 2024
This paper presents a comparison of the classification performance of a fuzzy clustering based support vector machine (SVM) with reinforcement learning and the previously developed fuzzy clustering based SVM for autocoding. While SVM is a machine leaning method known for high classification performance, there is an issue that SVM requires a large amount of processing time for large complex data. Therefore, in this study, we develop a classification method of fuzzy clustering based SVM with reinforcement learning to reduce the processing time for SVM. The purpose of this paper is comparing both classification performance and processing time of the developed fuzzy clustering based SVM with reinforcement learning and the ordinary fuzzy clustering based SVM. Numerical examples with governmental survey data, the Family Income and Expenditure survey data, show a better performance of the fuzzy clustering based SVM with reinforcement learning.