多概念学习的决策树MNID算法

New Decision Tree Algorithm MNID Based on Multiple Concepts

决策树归纳法ID3是人工智能机器学习中发展较快的一种归纳学习算法,而目前的ID3及其改进算法亦因各种问题限制了其在工程中的应用.基于决策树归纳学习算法示例学习最优化的理论,用在多概念学习条件下对多特征属性值进行分组聚类实现优化的方法,导出了定义在多概念空间上的决策树归纳学习算法MNID.这种新算法对工程技术领域普遍存在的多概念学习问题。

Decision tree algorithm ID3is an inductive learning algorithm developed quickly in machine learning technology ofartificial intelligence.Atpresent,the engineering application of ID3and itsimproved algorithm is limited by various engineering facts.With the help of the examples learning optimization prin- ciple of decision tree inductive learning algorithm,this paperadvanced a new decision tree algorithm MNID based on multiple concepts by adopting the gathering method of characteristic value groups.This new algo- rithm MNID is proved to be valuable for the problems of multiple concepts learning in the engineering ap- plication field.