Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes. This process is relevant when external constraints limit the performance of an industrial predictive system. Under such circumstances, copying enables the retention of original prediction capabilities while adapting to new demands. Previous research has focused on the single-pass implementation for copying. This paper introduces a novel sequential approach that significantly reduces the amount of computational resources needed to train or maintain a copy, leading to reduced maintenance costs for companies using machine learning models in production. The effectiveness of the sequential approach is demonstrated through experiments with synthetic and real-world datasets, showing significant reductions in time and resources, while maintaining or improving accuracy.

翻译：通过复制进行差异复制是指利用另一个具有增强特性和属性的模型来复现机器学习模型的决策行为。当外部约束限制工业预测系统的性能时，该过程具有相关性。在此类情况下，复制能够在适应新需求的同时保留原始预测能力。以往研究聚焦于单次实现的复制方法。本文提出了一种新颖的序列化方法，显著减少了训练或维护副本所需的计算资源，从而降低了在生产环境中使用机器学习模型的公司维护成本。通过合成数据集与真实数据集的实验证明，该序列化方法在保持或提升准确性的前提下，显著缩减了时间和资源消耗。