Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
DIFFER: A Propositionalization Approach for Learning from Structured Data
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 World Academy of Science, Engineering and Technology, ISSN 2010-376X, E-ISSN 2070-3740, Vol. 15, 49-51 p.Article in journal (Refereed) Published
Abstract [en]

Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

Place, publisher, year, edition, pages
2006. Vol. 15, 49-51 p.
Keyword [en]
WASET, Engineering, Technology
National Category
Engineering and Technology
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-101072OAI: oai:DiVA.org:su-101072DiVA: diva2:698678
Available from: 2014-02-24 Created: 2014-02-24 Last updated: 2017-12-05Bibliographically 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

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Karunaratne, ThashmeeBoström, Henrik
By organisation
Department of Computer and Systems Sciences
In the same journal
Proceedings of World Academy of Science, Engineering and Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 43 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf