Abstract
Feature selection employed for dimensionality reduction is an essential preprocessing task to guarantee high accuracy and efficiency of data analysis in practical applications. This paper proposes a consistency-based feature selection method for dimensionality reduction in incomplete data. The computational efficiency of the proposed feature selection method is improved by proposing a quick algorithm of computing the positive region based on the sorting and label techniques. Compared with the state-of-the-art feature selection methods, the proposed feature selection algorithm achieves less computational time for dimensionality reduction in incomplete data by the experimental results.
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Qian, W., Shu, W., Wang, Y. (2014). A Consistency-Based Dimensionality Reduction Algorithm in Incomplete Data. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_54
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DOI: https://doi.org/10.1007/978-3-319-11116-2_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
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