Abstract
In the field of data mining (DM), feature selection is one of the basic strategies handling with high-dimensionality problems. This paper makes a review of current methods of feature selection and proposes a unified strategy of feature selection, which divides overall procedures of feature selection into two stages, first to determine the FIF (Feature Important Factor) of features according to DM tasks, second to select features according to FIF. For classifying problems, we propose a new method for determining FIF based on decision trees and provide practical suggestion for feature selection. Through analysis on experiments conducted on UCI datasets, such a unified strategy of feature selection is proven to be effective and efficient.
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Liu, P., Wu, N., Zhu, J., Yin, J., Zhang, W. (2006). A Unified Strategy of Feature Selection. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_50
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DOI: https://doi.org/10.1007/11811305_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
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