{"title":"基于优化支持向量机的财务困境预测比较研究","authors":"Chun-Mei Liu","doi":"10.1109/ICMLC.2012.6358968","DOIUrl":null,"url":null,"abstract":"This paper investigates the development and modeling problem for financial distress prediction via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithm of Particle Swarm Optimization (PSO)-SVM is proposed for financial distress predicting process with strong coupling and nonlinear characteristics through the principle component analysis (PCA). Furthermore, Logistic regression (LR) algorithm is induced to make a comparison with Least-Square support vector machine (LS-SVM) and PSO-SVM. The simulation results show that the presented algorithms could get the satisfied accuracy effectively, and by contrast, PSO-SVM shows a better learning ability and generalization in financial distress predicting process modeling, and could establish predictive model with better accessibility.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative study of financial distress prediction via op timized SVM\",\"authors\":\"Chun-Mei Liu\",\"doi\":\"10.1109/ICMLC.2012.6358968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the development and modeling problem for financial distress prediction via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithm of Particle Swarm Optimization (PSO)-SVM is proposed for financial distress predicting process with strong coupling and nonlinear characteristics through the principle component analysis (PCA). Furthermore, Logistic regression (LR) algorithm is induced to make a comparison with Least-Square support vector machine (LS-SVM) and PSO-SVM. The simulation results show that the presented algorithms could get the satisfied accuracy effectively, and by contrast, PSO-SVM shows a better learning ability and generalization in financial distress predicting process modeling, and could establish predictive model with better accessibility.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6358968\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6358968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative study of financial distress prediction via op timized SVM
This paper investigates the development and modeling problem for financial distress prediction via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithm of Particle Swarm Optimization (PSO)-SVM is proposed for financial distress predicting process with strong coupling and nonlinear characteristics through the principle component analysis (PCA). Furthermore, Logistic regression (LR) algorithm is induced to make a comparison with Least-Square support vector machine (LS-SVM) and PSO-SVM. The simulation results show that the presented algorithms could get the satisfied accuracy effectively, and by contrast, PSO-SVM shows a better learning ability and generalization in financial distress predicting process modeling, and could establish predictive model with better accessibility.