Differentiable Model Selection for Ensemble Learning
Differentiable Model Selection for Ensemble Learning
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DOI：
10.24963/ijcai.2023/217
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
James Kotary;Vincenzo Di Vito;Ferdinando Fioretto
中科院分区：
文献类型：
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作者：
James Kotary;Vincenzo Di Vito;Ferdinando Fioretto
模型选择是一种旨在通过识别用于分类任何特定输入样本的最佳模型来创建准确和健壮的模型的策略。本文提出了一个新颖的框架，用于通过整合机器学习和组合优化来选择模型组的选择。该框架是针对合奏学习量身定制的，该策略将学习结合适当选择的预训练合奏模型的预测。它通过将整体学习任务建模为经过预定的合奏以优化任务性能的端到端的可区分选择程序进行建模。所提出的框架展示了其多功能性和有效性，在各种分类任务中都优于常规和高级共识规则。
Model selection is a strategy aimed at creating accurate and robust models by identifying the optimal model for classifying any particular input sample. This paper proposes a novel framework for differentiable selection of groups of models by integrating machine learning and combinatorial optimization. The framework is tailored for ensemble learning with a strategy that learns to combine the predictions of appropriately selected pre-trained ensemble models. It does so by modeling the ensemble learning task as a differentiable selection program trained end-to-end over a pretrained ensemble to optimize task performance. The proposed framework demonstrates its versatility and effectiveness, outperforming conventional and advanced consensus rules across a variety of classification tasks.