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

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

Lejeune, Rémi (TU Delft Electrical Engineering, Mathematics and Computer Science)

Dumančić, S. (mentor)
Hinnerichs, T.R. (mentor)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-25

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.

Machine Learning (ML)
Program Synthesis
A* search

http://resolver.tudelft.nl/uuid:e6fc0dbe-cb1a-4bec-9894-019afc6144e6

Student theses

bachelor thesis

© 2023 Rémi Lejeune