Solving ML with ML: Effectiveness of a star search for synthesizing machine learning pipelines

Bachelor thesis (2023)

Authors

R.J. Lejeune Electrical Engineering, Mathematics and Computer Science

Contributors

S. Dumančić Algorithmics - EEMCS (mentor)
T.R. Hinnerichs Algorithmics - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Rémi Lejeune
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Published Date

25-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper investigates the performance of the A* algorithm in the field of automated machine learning using program synthesis. We designed a context-free grammar to create machine learning pipelines and came up with a cost function for A*. Two different experiments were done, the first one to tune the parameters of our algorithm and the second one to compare the efficiency of A* with other search algorithms. The results indicate that for the selected datasets, A* did not have better performance, but rather had similar results with the other search algorithms. Nevertheless, more research in this field is needed to find concrete results.

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