A new data classification method based on chaotic particle swarm optimization and least square-support vector machine

Abstract

In order to improve the classification accuracy in chemometrics data, chaotic optimization algorithm (COA) and particle swarm optimization (PSO) algorithm are introduced into least square-support vector machine (LS-SVM) model in order to propose an optimized LS-SVM model and a novel data classification (CPL-SVM) method in this paper. In the proposed CPL-SVM method, the COA with the randomness and ergodicity is used to chaotically process the initial position and local extreme position of particle in the PSO algorithm in order to obtain a chaotic particle swarm optimization (CPSO) algorithm, and the CPSO is used to select and optimize the important parameters of LS-SVM, then the optimized parameters are used to obtain a better CPL-SVM classification method. The choice randomness of parameters is avoided and the selection workload of parameters is reduced. And this method can not only overcome the time-consuming and blindness of cross validation method, but also reflect small sample learning ability. In order to verify the effectiveness of CPL-SVM method, binary classification data, IRIS flower data and three relevant data sets with pharmacodynamic properties of drug are selected in this paper. The experiment results show that the proposed CPL-SVM method takes on the better learning performance, strong generalization ability, best sensitivity, Matthews correlation coefficient and classification accuracy. And it can effectively avoid the isolated effects of sample in the learning process.