A meta-combiner is a form of ensemble learning technique used with missing attribute values. Its common topology involves base learners and classifiers at the first level, and meta-learner and meta-classifier at the second level. The meta-classifier combines the decisions of all the base classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
(2017). Meta-combiner. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_536
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_536
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering