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Kalyan Veeramachaneni. Wang, Alex Christopher Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2016-01-15T20:47:18Z 2016-01-15T20:47:18Z 2015 2015 http://hdl.handle.net/1721.1/100859 932639544 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). 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. by Alex Christopher Wang. M.Eng. in Computer Science and Engineering 71 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Feature Factory : a collaborative, crowd-sourced machine learning system Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f3e74181-7ab1-4338-aff3-2b2ce95730a5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Feature Factory : a collaborative, crowd-sourced machine learning system</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Wang, Alex Christopher</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>