2026-09-18T17:24:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/855002026-06-06T00:54:39Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Catherine Havasi and Kevin C. Gold.
Sittig, Karen Anne
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:46:31Z
2014-03-06T15:46:31Z
2013
2013
http://hdl.handle.net/1721.1/85500
871002879
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-60).
Writing programs that are capable of completing complex tasks on web pages is difficult due to the inconsistent nature of the pages themselves. While there exist best practices for developing naming schemes for page elements, these schemes are not strictly enforced, making it difficult to develop a general-use automated system. Many pages must be hand-labeled if they are to be incorporated into an automated testing framework. In this thesis, I build an application that assists human users in classifying and labeling web pages. This system uses a gradient boosting classifier from the scikit-learn Python package to identify which of four tasks may be performed on a given web page. It also attempts to automatically label the input fields and buttons on the web page using a gradient boosting classifier. It outputs its results in a format that can be easily consumed by the LARIAT system at MIT Lincoln Laboratory, greatly reducing the human labor required to incorporate new web pages into the system.
by Karen Anne Sittig.
M.Eng.
60 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.
An affordance-inspired tool for automated web page labeling and classification
Automated web navigation via affordance learning
Thesis
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Publication
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<Abstract>Writing programs that are capable of completing complex tasks on web pages is difficult due to the inconsistent nature of the pages themselves. While there exist best practices for developing naming schemes for page elements, these schemes are not strictly enforced, making it difficult to develop a general-use automated system. Many pages must be hand-labeled if they are to be incorporated into an automated testing framework. In this thesis, I build an application that assists human users in classifying and labeling web pages. This system uses a gradient boosting classifier from the scikit-learn Python package to identify which of four tasks may be performed on a given web page. It also attempts to automatically label the input fields and buttons on the web page using a gradient boosting classifier. It outputs its results in a format that can be easily consumed by the LARIAT system at MIT Lincoln Laboratory, greatly reducing the human labor required to incorporate new web pages into the system.</Abstract>
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