CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression

TL;DR

CARTGen-IR introduces a novel, interpretable, and efficient synthetic sampling method based on decision trees to address imbalanced regression problems in tabular data, outperforming existing techniques.

Contribution

The paper presents a CART-based sampling approach tailored for imbalanced regression, avoiding thresholding and enabling realistic, transparent data generation.

Findings

Competitive with state-of-the-art methods in extreme-value prediction

Faster execution compared to deep generative models

Provides greater transparency and interpretability

Abstract

Handling imbalanced target distributions in regression poses a persistent challenge, as the underrepresentation of relevant target values can significantly hinder model performance. Existing data-level solutions often adapt classification-oriented techniques, introducing arbitrary thresholds over the continuous target and leading to artificial and potentially misleading problem formulations. Deep generative models offer flexible sample synthesis but are computationally intensive and difficult to interpret. We propose a CART-based synthetic sampling method specifically designed for imbalanced regression on tabular data. The method integrates relevance- and density-guided sampling to address sparse target regions without thresholding, and employs a feature-driven tree structure to generate realistic tabular samples across heterogeneous features and non-linear interactions. Experiments on benchmark datasets for extreme-value prediction show that the proposed approach is competitive with state-of-the-art resampling and generative methods while offering faster execution and greater transparency. These results highlight its potential as a scalable and interpretable data-level strategy for improving regression models in imbalanced domains.