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Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - a Case Study with Medicinal Chemistry Datasets
Stockholms universitet, Institutionen för data- och systemvetenskap.
Stockholms universitet, Sweden.
2010 (English)In: Ninth International Conference on Machine Learning and Applications (ICMLA), 2010: Proceedings, IEEE Computer Society, 2010, p. 828-833Conference paper, Published paper (Refereed)
Abstract [en]

Graph propositionalization methods can be used to transform structured and relational data into fixed-length feature vectors, enabling standard machine learning algorithms to be used for generating predictive models. It is however not clear how well different propositionalization methods work in conjunction with different standard machine learning algorithms. Three different graph propositionalization methods are investigated in conjunction with three standard learning algorithms: random forests, support vector machines and nearest neighbor classifiers. An experiment on 21 datasets from the domain of medicinal chemistry shows that the choice of propositionalization method may have a significant impact on the resulting accuracy. The empirical investigation further shows that for datasets from this domain, the use of the maximal frequent item set approach for propositionalization results in the most accurate classifiers, significantly outperforming the two other graph propositionalization methods considered in this study, SUBDUE and MOSS, for all three learning methods.

Place, publisher, year, edition, pages
IEEE Computer Society, 2010. p. 828-833
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221570DOI: 10.1109/ICMLA.2010.128Scopus ID: 2-s2.0-79952373267ISBN: 978-1-4244-9211-4 (print)OAI: oai:DiVA.org:kth-221570DiVA, id: diva2:1175238
Conference
Ninth International Conference on Machine Learning and Applications (ICMLA), 12-14 December 2010, Washington D.C., USA
Note

QC 20180202

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

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CiteExportLink to record
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  • apa
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Output format
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