基于级联Actor-Critic Agent的可追溯自动特征转换
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
摘要：
面向AI的特征转换是提高机器学习（ML）有效性和可解释性的重要任务。特征变换旨在对原始数据进行变换，以识别最佳特征空间，从而增强下游ML模型的性能。现有的研究要么结合预处理，特征选择和生成技能来经验地转换数据，要么通过机器智能自动化特征转换，例如强化学习。然而，现有的研究存在以下问题：1）高维非判别特征空间; 2）无法表示复杂的情境状态; 3）不能有效地整合局部和全局特征信息。为了填补这一研究空白，我们将特征转换任务表述为一个迭代的、嵌套的特征生成和选择过程，其中特征生成是在原始特征的基础上生成和添加新的特征，而特征选择是去除冗余特征以控制特征空间的大小。最后，我们提出了广泛的实验和案例研究，以说明24.7%的改善F1分数相比，SOTA和高维数据的鲁棒性。
英文摘要：
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
