[2501.10677] Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring

Computer Science > Machine Learning arXiv:2501.10677 (cs) [Submitted on 18 Jan 2025 (v1), last revised 29 Mar 2026 (this version, v3)] Title:Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring Authors:Xia Li, Hanghang Zheng, Xiwei Zhuang, Zhong Wang, Xiao Chen, Hong Liu, Jasmine Bai, Mao Mao View a PDF of the paper titled Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring, by Xia Li and 7 other authors View PDF Abstract:The advent of artificial intelligence has significantly enhanced credit scoring technologies. Despite the remarkable efficacy of advanced deep learning models, mainstream adoption continues to favor tree-structured models due to their robust predictive performance on tabular data. Although pretrained models have seen considerable development, their application within the financial realm predominantly revolves around question-answering tasks and the use of such models for tabular-structured credit scoring datasets remains largely unexplored. Tabular-oriented large models, such as TabPFN, has made the application of large models in credit scoring feasible, albeit can only processing with limited sample sizes. This paper provides a novel framework to combine tabular-tailored dataset distillation technique with the pretrained model, empowers the scalability for TabPFN. Furthermore, though class imbalance distribution is the common nature in financial datasets, its...