Abstract
Feature selection methods search for an “optimal” subset of features. Many methods exist. We evaluate consistency measure along with different search techniques applied in the literature and suggest a guideline of its use.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H. Almuallim and T. G. Dietterich. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1–2):279–305, November 1994.
M. Dash. Feature selection via set cover. In Proceedings of IEEE Knowledge and Data Engineering Exchange Eorkshop, pages 165–171, Newport, California, November 1997. IEEE Computer Society.
H. Liu, H. Motoda, and M. Dash. A monotonic measure for optimal feature selection. In Proceedings of European Conference on Machine Learning, pages 101–106, 1998.
H. Liu and R. Setiono. A probabilistic approach to feature selection-a filter solution. In Proceedings of International Conference on Machine Learning, pages 319–327, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dash, M., Liu, H., Motoda, H. (1999). Feature Selection Using Consistency Measure. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_30
Download citation
DOI: https://doi.org/10.1007/3-540-46846-3_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66713-1
Online ISBN: 978-3-540-46846-2
eBook Packages: Springer Book Archive