基于优化支持向量机的财务困境预测比较研究

Chun-Mei Liu
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摘要

本文研究了基于优化支持向量机(SVM)的财务困境预测的发展和建模问题。基于参数优化和模型选择思想,通过主成分分析(PCA),针对具有强耦合和非线性特征的财务困境预测过程,提出了粒子群优化(PSO)-支持向量机的群体智能算法。此外,引入Logistic回归(LR)算法与最小二乘支持向量机(LS-SVM)和PSO-SVM进行比较。仿真结果表明,所提算法能有效地获得满意的准确率,而PSO-SVM在财务困境预测过程建模中表现出更好的学习能力和泛化能力,建立的预测模型具有更好的可达性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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