Automatic feature augmentation ranking: XGBoost

Bachelor thesis (2022)

Authors

O.L.C. Neut Electrical Engineering, Mathematics and Computer Science

Contributors

A. Ionescu Web Information Systems - EEMCS (mentor)
R. Hai Web Information Systems - EEMCS (mentor)
D.H.J. Epema Data-Intensive Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Oliver Neut
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Published Date

20-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Automatic machine learning is a subfield of machine learning that automates the common procedures faced in predictive tasks. The problem of one such procedure is automatic data augmentation, where one desires to enrich the existing data to increase model performance. In relational data repositories, the data is stored in normal form. This causes problems, since joining all tables and subsequently performing feature selection is highly inefficient. This paper provides AFAR, an approach to efficiently and effectively perform automated feature augmentation by ranking candidate joins in a data repository. Additionally, an experimental evaluation that validates the approach’s capabilities, is presented.