Design of a fast learning classifier for sleep apnea database based on fuzzy SVM

Abstract

In this paper, we compared the performance of support vector machine (SVM) and fuzzy SVM (FSVM) for reduction of learning time when classifying large-scale time series data into two classes. The fast learning time of the pattern classifier for large time series data is very useful in decision support systems. Considering the high interest in healthcare, including big data analysis, it is necessary to design a pattern classifier with a fast learning capability. We used large-scale time series data of 32 patients with sleep apnea (SA) for this study. The experiment was conducted by extending the parameter n, of the fuzzy membership function of FSVM, from 1 to 500. The result shows that the shortest learning time of FSVM is 3 s for radial base function (RBF), 17 s for a polynomial, and 35 s for a linear kernel, where the parameter n of the fuzzy membership function is n = 2, n = 433, and n = 4, respectively. The maximum classification hit rate of FSVM is 93.23%, and the learning time is significantly faster than conventional SVM. Therefore, FSVM can be used as a good classifier for the large-scale time series SA database.