Join Path-Based Data Augmentation for Decision Trees

Join Path-Based Data Augmentation for Decision Trees

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)

O'Conner, L. (editor)

2022

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.

http://resolver.tudelft.nl/uuid:f3800d88-755e-46e8-927d-5e70188fc47d

https://doi.org/10.1109/ICDEW55742.2022.00018

IEEE, Piscataway

2023-07-01

978-1-6654-8105-2

Proceedings of the 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)

2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW), 2022-05-09, Kuala Lumpur, Malaysia

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 A. Ionescu, R. Hai, M. Fragkoulis, A Katsifodimos