An innovative one-class least squares support vector machine model based on continuous cognition

One-class classification is a basic problem in machine learning. Unlike the existing typical one-class classifiers designed from the angle of probability or geometric, this paper attempts to study this problem from the bionics point of view. Using the continuous cognition characteristic as the starting point, we propose a new framework of one-class classifier, named multiple regression model (OC-MR), which can be seen as a natural extension of multiple regression for one-class classification problem. This paper applies least squares support vector machine (LSSVM) as an example to show the modeling process of the proposed method and the corresponding one-class classifier is named one-class least squares support vector machine (OC-LSSVM). Various simulation and real-life datasets are used to test the performance of the proposed OC-LSSVM. The existing popular one-class classification methods including Parzen kernel density estimation, support vector data description and Gaussian mixture model are also applied in order to achieve a comprehensive comparison. The results show that OC-LSSVM has achieved the best performance in most of the simulation and real-life datasets due to its good robustness, which highlights the efficacy of OC-LSSVM.