Data collection plays an important role in business agility; data can prove valuable and provide insights for important features. However, conventional data collection methods can be costly and time-consuming. This paper proposes a hybrid system R-EDML that combines a sequential feature selection performed by Reinforcement Learning (RL) with the evolutionary feature prioritization of Evolutionary Distance Metric Learning (EDML) in a clustering process. The goal is to reduce the features while maintaining or increasing the accuracy leading to less time complexity and future data collection time and cost reduction. In this method, features represented by the diagonal elements of EDML matrices are prioritized using a differential evolution algorithm. Further, a selection control strategy using RL is learned by sequentially inserting and evaluating the prioritized elements. The outcome offers the best accuracy R-EDML matrix with the least number of elements. Diagonal R-EDML focusing on the diagonal elements is compared with EDML and conventional feature selection. Full Matrix R-EDML focusing on the diagonal and non-diagonal elements is tested and compared with Information-Theoretic Metric Learning. Moreover, R-EDML policy is tested for each EDML generation and across all generations. Results show a significant decrease in the number of features while maintaining or increasing accuracy.

中文翻译：

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基于增强学习的度量过滤用于进化距离度量学习

数据收集在业务敏捷性中起着重要作用。数据可以证明是有价值的，并可以为重要功能提供见解。但是，传统的数据收集方法可能既昂贵又耗时。本文提出了一种混合系统R-EDML，它在聚类过程中结合了强化学习（RL）进行的顺序特征选择和进化距离度量学习（EDML）的进化特征优先级排序。目的是在保持或提高精度的同时减少功能，从而减少时间复杂度并减少将来的数据收集时间并降低成本。在这种方法中，使用差分进化算法对由EDML矩阵的对角元素表示的特征进行优先排序。进一步，通过依次插入和评估优先元素，学习了使用RL的选择控制策略。结果提供了最少元素数的最佳精度R-EDML矩阵。将重点放在对角线元素上的对角线R-EDML与EDML和常规特征选择进行了比较。测试了专注于对角线和非对角线元素的Full Matrix R-EDML，并将其与信息理论度量学习进行了比较。而且，R-EDML策略针对每个EDML代以及所有代都进行了测试。结果表明，在保持或提高准确性的同时，特征数量显着减少。测试了专注于对角线和非对角线元素的Full Matrix R-EDML，并将其与信息理论度量学习进行了比较。而且，R-EDML策略针对每个EDML代以及所有代都进行了测试。结果表明，在保持或提高准确性的同时，特征数量显着减少。测试了专注于对角线和非对角线元素的Full Matrix R-EDML，并将其与信息理论度量学习进行了比较。而且，R-EDML策略针对每个EDML代以及所有代都进行了测试。结果表明，在保持或提高准确性的同时，特征数量显着减少。