基于GA参数寻优的决策树支持向量机生态环境质量评价方法

Evaluation of Eco-Environment Level Based on Decision-Tree-Based Support Vector Machine With Parameters Optimized by Genetic Algorithm

选取决策树作为支持向量机多类分类方法,选择径向基核函数建立了生态环境质量决策树支持向量机评价模型,基于遗传算法实现了惩罚因子、核函数参数的自适应优选,并运用建立的模型对我国主要省市生态环境质量进行了评价。研究结果表明,该方法可以较好地实现生态环境质量评价。

To apply support vector machine（SVM） for evaluation of eco-environment,it is essential to give priority to designing the classifier and picking kernel functions,their parameters and penalty factors.Described here are the ways of choosing decision-tree as SVM multi-class classification method and radical base kernel functions to build a decision-tree-based SVM evaluation model for eco-environment level.Self-adaptive optimization of penalty factors and kernel function parameters is realized.The evaluation model is tested for eco-environment evaluation of some major cities and provinces of China.The test demonstrates that SVM is a good method to evaluate eco-environmental quality.