ARDA: Automatic Relational Data Augmentation for Machine Learning
| Autor: | Nadiia Chepurko, Raul Fernandez, David R. Karger, Tim Kraska, Emanuel Zgraggen, Ryan Marcus |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Relational database Machine Learning (stat.ML) Feature selection 02 engineering and technology Information repository Machine learning computer.software_genre Machine Learning (cs.LG) Computer Science - Databases Statistics - Machine Learning 020204 information systems 0202 electrical engineering electronic engineering information engineering Hyperparameter business.industry Model selection General Engineering Databases (cs.DB) Pipeline (software) Data set Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | arXiv |
| Popis: | 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 ARDA, 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. |
| Databáze: | OpenAIRE |
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