基于粗糙变精度的食品安全决策树研究

食品安全决策是食品安全问题研究的一项重要内容。为了对食品安全状况进行分析，基于粗糙集变精度模型，提出了一种包含规则置信度的构造决策树新方法。这种新方法针对传统加权决策树生成算法进行了改进，新算法以加权平均变精度粗糙度作为属性选择标准构造决策树，用变精度近似精度来代替近似精度，可以在数据库中消除噪声冗余数据，并且能够忽略部分矛盾数据，保证决策树构建过程中能够兼容部分存在冲突的决策规则。该算法可以在生成决策树的过程中，简化其生成过程，提高其应用范围，并且有助于诠释其生成规则。验证结果表明该算法是有效可行的。

Research on Decision Tree for Food Safety Based on Variable Precision Rough Sets

Food safety decision is an important content of food safety research. Based on variable precision rough sets model, a method of building decision tree with rules that have definite confidence is proposed for food safety analysis. It is an improvement for decision tree inducing approach presented in traditional methods. Present a new algorithm for constructing decision tree with variable precision weighted mean roughness as the criteria for selecting attribute. The new algorithm used variable precision approximate accuracy instead the approxi- mate accuracy. Noisy data of training sets are considered enough. Limited inconsistency is allowed to existed examples of the positive re- gions. So the decision tree is simplified and its extensive ability is improved and more comprehensible. Experiments show that the algo- rithm is feasible and effective.