The traditional one-class support vector machines are sensitive to noise data and not suitable for large-scale classification. In order to solve the problem

a novel one-class fuzzy support vector machine based on reduced convex hull called OC-FSVM-RCH is proposed for large-scale noise data classification. According to the reduced convex hull

OC-FSVM-RCH obtains the samples representing the geometric characteristics of normal class data in the kernel space. Then OC-FSVM-RCH improves the fuzzy support vector domain description algorithm

in which normal class data is enclosed in the smallest hypersphere

and the margin between abnormal class data and hypersphere is maximized. OC-FSVM-RCH can eliminate the noise at the edge of normal data contour and is insensitive to the noise inside the normal data. Experimental results show that the proposed algorithm achieves good results in terms of performance and training time.