A linear multivariate binary decision tree classifier based on K-means splitting. (November 2020)

• Record Type: Journal Article
• Authors: Wang, Fei; Wang, Quan; Nie, Feiping; Li, Zhongheng; Yu, Weizhong; Ren, Fuji
• Is Part Of: Pattern recognition. Volume 107(2020:Nov.)
• Publication Date: 2020-11
• Subjects: Hierarchical classifier -- Binary tree -- Multivariate decision tree -- K-means -- Supervised classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107521
• Languages: English
• ISSNs: 0031-3203

Abstract

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. 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.