ARDA: automatic relational data augmentation for machine learning

Abstract

© 2020, VLDB Endowment. Automatic machine learning (AML) is a family of techniques to automate the process of training predictive models, aim-ing to both improve performance and make machine learn-ing more accessible. While many recent works have focused on aspects of the machine learning pipeline like model se-lection, hyperparameter tuning, and feature selection, rela-tively few works have focused on automatic data augmen-tation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal "human-in-the-loop" involvement. We present ARDA, an end-to-end system that takes as input a dataset and a data repository, and outputs an aug-mented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical eval-uation of different system components and benchmark our feature selection algorithm on real-world datasets.