Feature augmentation from one-to-many relationship tables is a critical but challenging problem in ML model development. To augment good features, data scientists need to come up with SQL queries manually, which is time-consuming. Featuretools [1] is a widely used tool by the data science community to automatically augment the training data by extracting new features from relevant tables. It represents each feature as a group-by aggregation SQL query on relevant tables and can automatically generate these SQL queries. However, it does not include predicates in these queries, which significantly limits its application in many real-world scenarios. To overcome this limitation, we propose FEATAUG, a new feature augmentation framework that automatically extracts predicate-aware SQL queries from one-to-many relationship tables. This extension is not trivial because considering predicates will exponentially increase the number of candidate queries. As a result, the original Featuretools framework, which materializes all candidate queries, will not work and needs to be redesigned. We formally define the problem and model it as a hyperparameter optimization problem. We discuss how the Bayesian Optimization can be applied here and propose a novel warm-up strategy to optimize it. To make our algorithm more practical, we also study how to identify promising attribute combinations for predicates. We show that how the beam search idea can partially solve the problem and propose several techniques to further optimize it. Our experiments on four real-world datasets demonstrate that FeatAug extracts more effective features compared to Featuretools and other baselines. The code is open-sourced at https://github.com/sfu-db/FeatAug
Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006
Mohamed Khider University（穆罕默德·希德尔大学）；Norwegian University of Science and Technology（挪威科技大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Gulf University for Science & Technology（海湾科技大学）
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
Department of Computer Science University of Freiburg（计算机科学系弗赖堡大学）；University of Freiburg（弗赖堡大学）；Prior Labs ELLIS Institute Tübingen University of Freiburg（Prior实验室ELLIS研究所弗赖堡大学）
Generative Compositional Augmentations for Scene Graph Prediction
University of Guelph（圭尔夫大学）；Vector Institute for Artificial Intelligence（人工智能向量研究所）；Element AI（元素人工智能公司）；University of Cambridge（剑桥大学）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；Concordia University（康考迪亚大学）
