Feature Factory : a collaborative, crowd-sourced machine learning system
Name
932639544-MIT.pdf
Description
Full printable version
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
da0ae05a4cecbd2cc6d4d20fe710be86
Author(s)
Wang, Alex Christopher
Advisor(s)
Kalyan Veeramachaneni.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I designed, implemented, and tested a machine learning learning system designed to crowd-source feature discovery called Feature Factory. Feature Factory provides a complete web-based platform for users to define, extract, and test features on any given machine learning problem. This project involved designing, implementing, and testing a proof-of-concept version of this platform. Creating the platform involved developing user-side infrastructure and system-side infrastructure. The user-side infrastructure required careful design decisions to provide users with a clear and concise interface and workflow. The system-side infrastructure involved constructing an automated feature aggregation, extraction, and testing pipeline that can be executed with a few simple commands. Testing was performed by presenting three different machine learning problems to test users via the user-side infrastructure of Feature Factory. Users were asked to write features for the three different machine learning problems as well as comment on the usability of the system. The systemside infrastructure was utilized to analyze the effectiveness and performance of the features written by the users.
Description
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
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 (page 71).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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