Graph propositionalization for random forests
2009 (English)In: The Eighth International Conference on Machine Learning and Applications: Proceedings, IEEE Computer Society, 2009, 196-201 p.Conference paper (Refereed)
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. 196-201 p.
Engineering and Technology
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-101075DOI: 10.1109/ICMLA.2009.113OAI: oai:DiVA.org:su-101075DiVA: diva2:698683
The Eighth International Conference on Machine Learning and Applications (ICMLA), Miami Beach, Florida, 13 - 15 December 2009