Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - A Case Study with Medicinal Chemistry Datasets

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 con...

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Veröffentlicht in:	2010 International Conference on Machine Learning and Applications S. 828 - 833
Hauptverfasser:	Karunaratne, Thashmee, Bostrom, Henrik, Norinder, U
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2010
Schlagworte:	Chemistry Classification algorithms Computer and systems science Computer and Systems Sciences Data mining Data- och systemvetenskap graph propositionalization Informatics, computer and systems science Informatik, data- och systemvetenskap Itemsets k-nearest neighbor Kernel Machine learning Machine learning algorithms medicinal chemistry random forests SAMHÄLLSVETENSKAP SOCIAL SCIENCES Statistics, computer and systems science Statistik, data- och systemvetenskap structured data support-vector machines
ISBN:	1424492114, 9781424492114
Online-Zugang:	Volltext
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