title: PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.
abstract: Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.
authors: Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China ; School of Business Administration, South China University of Technology, Guangzhou, Guangdong 510640, China.
authors: Xiaoyong
authors: 0000-0002-0795-841X
authors: X
authors: Liu
authors: Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China.
authors: Hui
authors: H
authors: Fu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019937:Diagnostic Techniques and Procedures; D006801:Humans; D008962:Models, Theoretical; D060388:Support Vector Machine