Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises

2025/08/30 by Lu Han, Xiuying Wang, Han, Lu +1
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Computer Vision and Pattern Recognition (cs.CV) #Credit Risk and Financial Regulations #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2509.00550

openalex publication_date 2025/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Traditional decision tree models, which rely exclusively on numerical variables, often face challenges in handling high-dimensional data and are limited in their ability to incorporate textual information effectively. To address these limitations, we propose the integrated multivariate segmentation tree (IMST), a comprehensive framework designed to improve credit evaluation for small- and medium-sized enterprises (SMEs) by integrating financial data with textual sources. This method comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization, (2) selecting salient financial features using Lasso regression, and (3) constructing a multivariate segmentation tree based on either the Gini index or entropy, with weakest-link pruning applied to control model complexity. Experimental results based on a dataset of 1,428 Chinese SMEs demonstrated that IMST achieved an accuracy rate of 88.9%, surpassing both baseline decision trees (87.4%) and conventional models such as support vector machines and neural networks. Furthermore, the proposed model demonstrated superior interpretability and computational efficiency, featuring a more streamlined architecture and improved risk detection capabilities.

Citations

• Multimodal Alignment and Fusion: A Survey
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
• Classification Based on Decision Tree Algorithm for Machine Learning
• A Survey on Deep Learning for Multimodal Data Fusion

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