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

•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.