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Graph propositionalization for random forests
Stockholms universitet, Institutionen för data- och systemvetenskap.
Stockholms universitet, Institutionen för data- och systemvetenskap.
2009 (English)In: The Eighth International Conference on Machine Learning and Applications: Proceedings, IEEE Computer Society , 2009, p. 196-201Conference paper, Published paper (Refereed)
Abstract [en]

Graph propositionalization methods transform structured and relational data into a fixed-length feature vector format that can be used by standard machine learning methods. However, the choice of propositionalization method may have a significant impact on the performance of the resulting classifier. Six different propositionalization methods are evaluated when used in conjunction with random forests. The empirical evaluation shows that the choice of propositionalization method has a significant impact on the resulting accuracy for structured data sets. The results furthermore show that the maximum frequent itemset approach and a combination of this approach and maximal common substructures turn out to be the most successful propositionalization methods for structured data, each significantly outperforming the four other considered methods.

Place, publisher, year, edition, pages
IEEE Computer Society , 2009. p. 196-201
National Category
Engineering and Technology
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221561DOI: 10.1109/ICMLA.2009.113ISI: 000291011600028Scopus ID: 2-s2.0-77950799502OAI: oai:DiVA.org:kth-221561DiVA, id: diva2:1175242
Conference
The Eighth International Conference on Machine Learning and Applications (ICMLA), Miami Beach, Florida, 13 - 15 December 2009
Note

QC 20180122

Available from: 2014-02-24 Created: 2018-01-17 Last updated: 2018-01-22Bibliographically approved

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Citation style
  • apa
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  • nn-NB
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  • Other locale
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Output format
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  • text
  • asciidoc
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