FeatureEnVi：基于逐步选择与半自动提取方法的特征工程可视化分析
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
AI总结：
本文提出FeatureEnVi，一种可视化分析系统，通过逐步选择与半自动提取方法辅助特征工程，帮助用户选择重要特征、转换特征并探索其影响，提升ML模型性能。
摘要：
机器学习（ML）生命周期涉及一系列迭代步骤，从有效收集和准备数据（包括复杂的特征工程过程），到结果的展示与改进，每一步都有多种算法可供选择。特征工程尤其对机器学习非常有益，可带来诸多改进，如提升预测结果、减少计算时间、降低过多噪声，以及增加训练过程中所做决策的透明度。尽管如此，尽管存在多种可视化分析工具来监控和控制机器学习生命周期的不同阶段（尤其是与数据和算法相关的阶段），特征工程的支持仍然不足。在本文中，我们提出了FeatureEnVi，一个专门设计用于辅助特征工程过程的可视化分析系统。我们提出的系统帮助用户选择最重要的特征，将原始特征转换为强大的替代方案，并试验不同的特征生成组合。此外，数据空间切片允许用户在局部和全局尺度上探索特征的影响。FeatureEnVi利用多种自动特征选择技术；此外，它通过统计证据直观地引导用户了解每个特征（或特征子集）的影响。最终结果是提取经过深度工程化的特征，并通过多种验证指标进行评估。FeatureEnVi的实用性和适用性通过两个使用案例和一个案例研究得到展示。我们还报告了与两位机器学习专家和一位可视化研究者的访谈反馈，他们评估了我们系统的有效性。
英文摘要：
The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data, including complex feature engineering processes, to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
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。
