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.

[2021-10-09] Accepted Paper /doc/2021.06.0294/v1

[2022-01-05] Printed Article /doc/2021.06.0294/v2