Abstract
Feature selection, the job to select features relevant to classification, is a central problem of machine learning. Inconsistency rate is known as an effective measure to evaluate consistency (relevance) of feature subsets, and INTERACT, a state-of-the-art feature selection algorithm, takes advantage of it. In this paper, we shows that inconsistency rate is not the unique measure of consistency by introducing two new consistency measures, and also, show that INTERACT has the important deficiency that it fails for particular types of probability distributions. To fix the deficiency, we propose two new algorithms, which have flexibility of taking advantage of any of the new measures as well as inconsistency rate. Furthermore, through experiments, we compare the three consistency measures, and prove effectiveness of the new algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance and min-redundancy. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(8) (2005)
Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: International Conference of Machine Learning (2003)
Biesiada, J., Duch, W.: Feature selection for high-dimensional data – a Kolmogorov-Smirnov correlation-based filter. Advances in Soft Computing 30, 95–103 (2005)
Biesiada, J., Duch, W.: Feature selection for high-dimensional data – a Pearson redundancy based filter. Advances in Soft Computing 45, 242–249 (2008)
Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155–176 (2003)
Almuallim, H., Dietterich, T.G.: Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence 69(1-2) (1994)
Zhao, Z., Liu, H.: Searching for interacting features. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 1156–1161 (2007)
Blake, C.S., Merz, C.J.: UCI repository of machine learning databases. Technical report. University of California, Irvine (1998)
IEEEWorld Congress on Computational Intelligence. Performance prediction challenge (2006), http://www.modelselect.inf.ethz.ch/
Witten, J.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Mogan Kaufmann Publishers, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shin, K., Xu, X.M. (2009). Consistency-Based Feature Selection. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04595-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-04595-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04594-3
Online ISBN: 978-3-642-04595-0
eBook Packages: Computer ScienceComputer Science (R0)