A linear multivariate binary decision tree classifier based on K-means splitting

作者：

Highlights：

• Introducing the unsupervised K-means clustering into supervised tree classification framework improves the classification performance.

• The proposed non-split condition enables the tree model to accommodate the class imbalance cases more flexibly.

• The proposed tree model is efficient during training and classification.

摘要

•Introducing the unsupervised K-means clustering into supervised tree classification framework improves the classification performance.•The proposed non-split condition enables the tree model to accommodate the class imbalance cases more flexibly.•The proposed tree model is efficient during training and classification.

论文关键词：Hierarchical classifier,Binary tree,Multivariate decision tree,K-means,Supervised classification

论文评审过程：Received 19 September 2019, Revised 15 April 2020, Accepted 23 June 2020, Available online 24 June 2020, Version of Record 3 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107521