邻域等价关系诱导的改进ID3决策树算法

经典ID3决策树算法适用于离散型数据分类,但用于连续处理时需要数据离散化容易导致信息损失。提出邻域等价关系从而诱导邻域ID3(NID3)决策树算法,NID3算法改进了ID3决策树算法,能够直接实施连续预测并获取更好的分类效果。在邻域决策系统中,挖掘一种邻域等价关系;基于邻域等价粒化,构建邻域信息度量;基于邻域信息增益,设计NID3决策树算法。实例分析与数据实验均表明,NID3算法具有连续数据分类预测有效性,在分类机器学习中优于ID3算法。

Improved ID3 decision tree algorithm induced by neighborhood equivalence relation

This paper applied the classical ID3 decision tree algorithm for discrete data classification. However, it required data discretization for continuous data processing, and this process easily caused information loss. This paper proposed the neighborhood equivalence relationship to induce the neighborhood ID3(NID3) decision tree algorithm. The NID3 algorithm improved the ID3 decision tree algorithm, which could directly implement continuous prediction and obtained better classification results. In the neighborhood decision system, this paper mined a neighborhood equivalence relationship. Then, based on the equivalent granulation of the neighborhood, this paper constructed the neighborhood information metric. Finally, this paper designed the NID3 decision tree algorithm based on the neighborhood information gain. As verified by both case analyses and data experiments, algorithm NID3 is effective for continuous data classification, and it outperforms ID3 algorithm in classification machine learning.