Feature Factory : a collaborative, crowd-sourced machine learning system

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Wang, Alex Christopher

Advisor dc:contributor.advisor

• Kalyan Veeramachaneni.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/100859

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/100859