Title
Join Path-Based Data Augmentation for Decision Trees
Author
Ionescu, A. (TU Delft Web Information Systems)
Hai, R. (TU Delft Web Information Systems)
Fragkoulis, M. (Delivery Hero SE) 
Katsifodimos, A (TU Delft Web Information Systems)
Contributor
O'Conner, L. (editor)
Date
2022
Abstract
Machine Learning (ML) applications require high-quality datasets. Automated data augmentation techniques can help increase the richness of training data, thus increasing the ML model accuracy. Existing solutions focus on efficiency and ML model accuracy but do not exploit the richness of dataset relationships. With relational data, the challenge lies in identifying join paths that best augment a feature table to increase the performance of a model. In this paper we propose a two-step, automated data augmentation approach for relational data that involves: (i) enumerating join paths of various lengths given a base table and (ii) ranking the join paths using filter methods for feature selection. We show that our approach can improve prediction accuracy and reduce runtime compared to the baseline approach.
To reference this document use:
http://resolver.tudelft.nl/uuid:f3800d88-755e-46e8-927d-5e70188fc47d
DOI
https://doi.org/10.1109/ICDEW55742.2022.00018
Publisher
IEEE, Piscataway
Embargo date
2023-07-01
ISBN
978-1-6654-8105-2
Source
Proceedings of the 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)
Event
2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW), 2022-05-09, Kuala Lumpur, Malaysia
Bibliographical note
Accepted Author Manuscript
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2022 A. Ionescu, R. Hai, M. Fragkoulis, A Katsifodimos