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

Fei Wang, Quan Wang, Feiping Nie, Zhongheng Li, Weizhong Yu, Fuji Ren

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is recursively integrated into the binary tree, building a hierarchical classifier. The introduction of the unsupervised K-means clustering provides the powerful generalization ability for the resulting BDTKS model. Then, the good generalization ability of BDTKS ensures the classification performance. A novel non-split condition with an easy-setting hyperparameter which focuses more on minority classes of the current node is proposed and applied in the BDTKS model, avoiding ignoring the minority classes in the class imbalance cases. Furthermore, the K-means centroid based BDTKS model is converted into the hyperplane based decision tree, speeding up the process of classification. Extensive experiments on the publicly available data sets have demonstrated that the proposed BDTKS matches or outperforms the previous decision trees.

Original languageEnglish
Article number107521
JournalPattern Recognition
Volume107
DOIs
StatePublished - Nov 2020

Keywords

  • Binary tree
  • Hierarchical classifier
  • K-means
  • Multivariate decision tree
  • Supervised classification

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