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Feb, 2025
基于梯度下降的紧凑规则分类器学习
Compact Rule-Based Classifier Learning via Gradient Descent
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Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez
TL;DR
本研究解决了规则模型在可扩展性和优化方面的挑战,提出了一种新型的基于梯度下降训练的规则分类器。该方法允许用户控制规则的数量和长度,实验结果表明,该分类器在准确性和规则库大小方面优于其他主流可解释分类器,能够生成更紧凑的规则库。
Abstract
Rule-based models play a crucial role in scenarios that require transparency and accountable decision-making. However, they primarily consist of discrete parameters and structures, which presents challenges for
Scalability
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