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Learning to Classify Structured Data by Graph Propositionalization
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2006 (engelsk)Inngår i: Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Existing methods for learning from structured data are limited with respect to handling large or isolated substructures and also impose constraints on search depth and induced structure length. An approach to learning from structured data using a graph based propositionalization method, called finger printing, is introduced that addresses the limitations of current methods. The method is implemented in a system called DIFFER, which is demonstrated to compare favorable to existing state-of-art methods on some benchmark data sets. It is shown that further improvements can be obtained by combining the features generated by finger printing with features generated by previous methods.

sted, utgiver, år, opplag, sider
2006.
Emneord [en]
Machine Learning, Graph, Classification, Structured data
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-38408OAI: oai:DiVA.org:su-38408DiVA: diva2:310286
Konferanse
The IASTED International Conference on Computational Intelligence, November 20 – 22, 2006, San Francisco, USA
Tilgjengelig fra: 2010-04-13 Laget: 2010-04-13 Sist oppdatert: 2014-02-26bibliografisk kontrollert
Inngår i avhandling
1. Learning predictive models from graph data using pattern mining
Åpne denne publikasjonen i ny fane eller vindu >>Learning predictive models from graph data using pattern mining
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2014. 118 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 14-003
Emneord
Machine Learning, Graph Data, Pattern Mining, Classification, Regression, Predictive Models
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-100713 (URN)978-91-7447-837-2 (ISBN)
Disputas
2014-03-25, room B, Forum, Isafjordsgatan 39, Kista, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-03-03 Laget: 2014-02-11 Sist oppdatert: 2014-03-04bibliografisk kontrollert

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