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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Vydané v:2010 International Conference on Machine Learning and Applications s. 828 - 833
Hlavní autori: Karunaratne, Thashmee, Bostrom, Henrik, Norinder, U
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2010
Predmet:
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
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Popis
Shrnutí: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.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.128