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...

Full description

Saved in:

Bibliographic Details
Published in:	2010 International Conference on Machine Learning and Applications pp. 828 - 833
Main Authors:	Karunaratne, Thashmee, Bostrom, Henrik, Norinder, U
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2010
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!