Abstract
Data dimensionality is growing rapidly, which poses challenges to the vast majority of existing mining and learning algorithms, such as the curse of dimensionality, large storage requirement, and high computational cost. Feature selection has been proven to be an effective and efficient way to prepare high-dimensional data for data mining and machine learning. The recent emergence of novel techniques and new types of data and features not only advances existing feature selection research but also evolves feature selection continually, becoming applicable to a broader range of applications. In this entry, we aim to provide a basic introduction to feature selection including basic concepts, classifications of existing systems, recent development, and applications.
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Wang, S., Tang, J., Liu, H. (2016). Feature Selection. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_101-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_101-1
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