ARDA: Automatic Relational Data Augmentation for Machine Learning

TL;DR

ARDA is an automated system that enhances datasets by intelligently joining and selecting features from data repositories, leading to improved machine learning model performance with minimal human intervention.

Contribution

The paper introduces ARDA, an end-to-end system for automatic data augmentation through data joining and feature selection, addressing a less-explored aspect of automated machine learning.

Findings

ARDA improves predictive performance across multiple datasets.

The feature selection algorithm effectively filters noisy and irrelevant features.

Benchmark results demonstrate ARDA's efficiency and effectiveness.

Abstract

Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal ``human-in-the-loop'' involvement. We present \system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented 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 evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.