Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality data throughout the entire life-cycle of such applications. Nevertheless, most of the real-world tabular data suffer from different types of discrepancies, such as missing values, outliers, duplicates, pattern violation, and inconsistencies. Such discrepancies typically emerge while collecting, transferring, storing, and/or integrating the data. To deal with these discrepancies, numerous data cleaning methods have been introduced. However, the majority of such methods broadly overlook the requirements imposed by downstream ML models. As a result, the potential of utilizing these data cleaning methods in ML pipelines is predominantly unrevealed. In this work, we introduce a comprehensive benchmark, called REIN1, to thoroughly investigate the impact of data cleaning methods on various ML models. Through the benchmark, we provide answers to important research questions, e.g., where and whether data cleaning is a necessary step in ML pipelines. To this end, the benchmark examines 38 simple and advanced error detection and repair methods. To evaluate these methods, we utilized a wide collection of ML models trained on 14 publicly-available datasets covering different domains and encompassing realistic as well as synthetic error profiles.
A Reliable and Efficient Detection Pipeline for Rodent Ultrasonic Vocalizations
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；Institute for Mind and Brain, University of South Carolina（南卡罗来纳大学心智与大脑研究所）；Carolina Autism and Neurodevelopment Research Center, University of South Carolina（南卡罗来纳大学卡罗莱纳自闭症与神经发育研究中心）；University of South Carolina School of Medicine（南卡罗来纳大学医学院）；Columbia VA Health Care System（哥伦比亚退伍军人事务医疗系统）
LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）
