Programming-by-example for data transformation to improve machine learning performance
Minori Narita, Takeo Igarashi · 2019
In this study, we propose a programming-by-example (PBE)-based data transformation method for feature engineering in machine learning. Data transformation by PBE is not new. However, we utilized the one proposed herein to improve the performance of machine learning in synthesizing a transformation rule from examples. Herein, the system first generates candidate rules, and then chooses the rule that achieves the highest performance in a target machine learning task. We tested this system with the Titanic dataset, and the result shows that the proposed method can avoid worst-case performance compared to the original PBE method.