AutoFE : efficient and robust automated feature engineering
Name
1080934990-MIT.pdf
Description
Full printable version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
ad2aefb12a31830b7abbe29d1b6c17a6
Author(s)
Song, Hyunjoon
Advisor(s)
Samuel Madden.
Alternative Title
Automate feature engineering : efficient and robust automated feature engineering
Efficient and robust automated feature engineering
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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..
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
