基于知识的模型自动选择策略

模型自动选择是决策支持系统智能化发展的必然要求。针对目前实用算法较少的现状，提出一种模型自动选择策略。基于知识框架描述模型，根据事实库和知识库提取相应规则生成推理树，结合经验和专业知识实现模型自动选择。实验结果表明，该策略具有较高的命中率。

Knowledge-based Automatic Model Selection Strategy

Aiming at the problems of complex and low efficiency decision tree constructed by ID3, this paper proposes a decision tree classification algorithm based on rough set, which takes the weighted classification rough degree as the heuristic function of choosing attribute at a node. This heuristic function can synthetically measure contribution of an attribute for classification, and is simple in calculation. To eliminate the effect of noise data on choosing attributes and generating leaf nodes, a method using variable precision rough set model is used to optimize the algorithm. Experimental results show that the size of trees generated by the new algorithm is smaller and higher accuracy than ID3 algorithm.