针对区间值预测难题，提出新模型并对比性能表现。
Interval Regression: A Comparative Study with Proposed Models
- 提出新型区间回归模型用于处理不确定目标值
- 实测表明无模型在所有场景下最优
- 适合需处理区间输出的工业预测任务
回归模型广泛应用于现实世界。然而，实际中目标值常不精确，而以可接受值区间形式存在。为此发展出区间回归模型。本研究全面回顾现有模型，并提出替代模型进行对比分析。在真实与合成数据集上开展实验，展示模型性能差异。结果表明，不存在普遍最优模型，强调需根据具体场景选择最适模型。
原文摘要 · Abstract (English)
Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.
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