Join Path-Based Data Augmentation for Decision Trees

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.