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Learning from Structured Data by Finger Printing
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2006 (English)In: Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), Helsinki: Finnish Artificial Intelligence society FAIS , 2006, 120-126 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Helsinki: Finnish Artificial Intelligence society FAIS , 2006. 120-126 p.
Series
Publications of the Finnish Artificial Intelligence Society, ISSN 1238-4658 ; 22
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-37961ISBN: 978-952-5677-00-3 (print)OAI: oai:DiVA.org:su-37961DiVA: diva2:305599
Conference
The Ninth Scandinavian Conference on Artificial Intelligence, Helsinki University of Technology Espoo, Finland, October 25-27, 2006
Available from: 2011-01-18 Created: 2010-03-24 Last updated: 2014-02-26Bibliographically approved
In thesis
1. Learning predictive models from graph data using pattern mining
Open this publication in new window or tab >>Learning predictive models from graph data using pattern mining
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Learning from graphs has become a popular research area due to the ubiquity of graph data representing web pages, molecules, social networks, protein interaction networks etc. However, standard graph learning approaches are often challenged by the computational cost involved in the learning process, due to the richness of the representation. Attempts made to improve their efficiency are often associated with the risk of degrading the performance of the predictive models, creating tradeoffs between the efficiency and effectiveness of the learning. Such a situation is analogous to an optimization problem with two objectives, efficiency and effectiveness, where improving one objective without the other objective being worse off is a better solution, called a Pareto improvement. In this thesis, it is investigated how to improve the efficiency and effectiveness of learning from graph data using pattern mining methods. Two objectives are set where one concerns how to improve the efficiency of pattern mining without reducing the predictive performance of the learning models, and the other objective concerns how to improve predictive performance without increasing the complexity of pattern mining. The employed research method mainly follows a design science approach, including the development and evaluation of artifacts. The contributions of this thesis include a data representation language that can be characterized as a form in between sequences and itemsets, where the graph information is embedded within items. Several studies, each of which look for Pareto improvements in efficiency and effectiveness are conducted using sets of small graphs. Summarizing the findings, some of the proposed methods, namely maximal frequent itemset mining and constraint based itemset mining, result in a dramatically increased efficiency of learning, without decreasing the predictive performance of the resulting models. It is also shown that additional background knowledge can be used to enhance the performance of the predictive models, without increasing the complexity of the graphs.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 118 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-003
Keyword
Machine Learning, Graph Data, Pattern Mining, Classification, Regression, Predictive Models
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-100713 (URN)978-91-7447-837-2 (ISBN)
Public defence
2014-03-25, room B, Forum, Isafjordsgatan 39, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2014-03-03 Created: 2014-02-11 Last updated: 2014-03-04Bibliographically approved

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Karunaratne, ThashmeeBoström, Henrik
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CiteExportLink to record
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