Efficient and robust automated feature engineering

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 59-61).Feature engineering is the key to building highly successful machine learning models. We present AutoFE, a system designed to automate feature engineering. AutoFE generates a large set of new interpretable features by combining information in the original features. Given an augmented dataset, it discovers a set of features that significantly improves the performance of any traditional classification using an evolutionary algorithm. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 8 datasets and 5 different classification algorithms. We show that AutoFE can achieve an average improvement in predictive performance of 25.24% for all classification algorithms over their baseline performance obtained with the original features..by Hyunjoon Song.M. Eng