Abstract
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well–known data sets, coupled with notable dimensionality reduction.
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.
Similar content being viewed by others
References
Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. In: Greiner, R., Subramanian, D. (eds.) Artificial Intelligence on Relevance, vol. 97, pp. 245–271 (1997)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. on Knowledge and Data Engineering 17, 1–12 (2005)
Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intalligence 1-2, 273–324 (1997)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Liu, H., Setiono, R.: A probabilistic approach to feature selection: a filter solution. In: 13th Inter. Conf. on Machine Learning, pp. 319–327. Morgan Kaufmann, San Francisco (1996)
Dash, M., Liu, H., Motoda, H.: Consistency based feature selection. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 98–109 (2000)
Almuallim, H., Dietterich, T.: Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence 69, 279–305 (1994)
Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: 17th International Conf. on Machine Learning, pp. 359–366. Morgan Kaufmann, San Francisco (2000)
Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of machine learning research 5, 1205–1224 (2004)
Inza, I., Larrañaga, P., Blanco, R., Cerrolaza, A.: Filter versus wrapper gene selection approaches in dna microarray domains. Artificial Intelligence in Medicine 31, 91–103 (2004)
Xiong, M., Fang, X., Zhao, J.: Biomarker identification by feature wrappers. Genome Res. 11, 1878–1887 (2001)
Xing, E., Jordan, M., Karp, R.: Feature selection for high-dimensional genomic microarray data. In: Proc. 18th International Conf. on Machine Learning, pp. 601–608. Morgan Kaufmann, San Francisco (2001)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machine. Machine Learning 46, 389–422 (2002)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S. (2005). Heuristic Search over a Ranking for Feature Selection. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_91
Download citation
DOI: https://doi.org/10.1007/11494669_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)