关键词: 支持向量机, 决策树支持向量机, 地下水, 水质评价

Abstract: Support Vector Machine (SVM) based on the minimum of structured risk is characterized with strong ability to learn and predict and favorable classification performance, which makes it able to solve the two types of pattern recognition of fewer sample learning. In order to evaluate the groundwater quality which has five classes with SVM, the decision-tree-based way of rebuilding the classes like decision tree to create more sub two-class SVM would be used. But as a solution of classifying more classes, some defaults exist in decision-tree-based support vector machine (DTBSVM) including the local error produced by different sample mount between two classes. The authors brought forward an optimized DTBSVM model based on the principle of two cross tree to realize the evaluation for groundwater quality. The experimental results show that the optimized DTBSVM model is a good way to evaluate the groundwater quality.

Key words: Support Vector Machine (SVM), Decision-Tree-Based SVM (DTBSVM), groundwater, water quality evaluation