用几何算法优化分类堆叠，提升准确率且无需调参。
Enhancing binary classification: A new stacking method via leveraging computational geometry
- 用最大加权矩形问题求解构建新元模型
- 在多个数据集上优于当前最优堆叠方法
- 解释性强，适合医疗金融等实际场景
堆叠是一种强大的集成学习方法，通过元模型整合多个基模型的优势以提升预测准确性。传统堆叠通常使用逻辑回归等成熟模型作为元模型。本文提出一种新方法，将计算几何技术（具体为求解最大加权矩形问题）融入元模型构建中，用于二分类任务。该方法在多个公开数据集上进行评估，统计分析显示其具有稳定性，并在使用留出折叠预测时，相比现有最先进堆叠方法实现了准确率提升。该新堆叠方法还具备两大显著优势：增强的可解释性，以及无需对元模型进行超参数调优，从而提升了实用性。这些优点使其不仅适用于堆叠集成学习，也广泛适用于医院健康评分、银行信用评分等真实场景，提供新的评估视角。
原文摘要 · Abstract (English)
Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such as logistic regression, as the meta-model. This paper introduces a novel approach that integrates computational geometry techniques, specifically solving the maximum weighted rectangle problem, to develop a new meta-model for binary classification. Our method is evaluated on multiple open datasets, with statistical analysis showing its stability and demonstrating improvements in accuracy compared to current state-of-the-art stacking methods with out-of-fold predictions. This new stacking method also boasts two significant advantages: enhanced interpretability and the elimination of hyperparameter tuning for the meta-model, thus increasing its practicality. These merits make our method highly applicable not only in stacking ensemble learning but also in various real-world applications, such as hospital health evaluation scoring and bank credit scoring systems, offering a fresh evaluation perspective.
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