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Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning

Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning

Amparo Alonso-Betanzos, Verónica Bolón-Canedo, Diego Fernández-Francos, Iago Porto-Díaz, Noelia Sánchez-Maroño
Copyright: © 2013 |Pages: 26
ISBN13: 9781466639423|ISBN10: 1466639423|EISBN13: 9781466639430
DOI: 10.4018/978-1-4666-3942-3.ch001
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MLA

Alonso-Betanzos, Amparo, et al. "Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning." Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, IGI Global, 2013, pp. 1-26. https://doi.org/10.4018/978-1-4666-3942-3.ch001

APA

Alonso-Betanzos, A., Bolón-Canedo, V., Fernández-Francos, D., Porto-Díaz, I., & Sánchez-Maroño, N. (2013). Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning. In B. Igelnik & J. Zurada (Eds.), Efficiency and Scalability Methods for Computational Intellect (pp. 1-26). IGI Global. https://doi.org/10.4018/978-1-4666-3942-3.ch001

Chicago

Alonso-Betanzos, Amparo, et al. "Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning." In Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, 1-26. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3942-3.ch001

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Abstract

With the advent of high dimensionality, machine learning researchers are now interested not only in accuracy, but also in scalability of algorithms. When dealing with large databases, pre-processing techniques are required to reduce input dimensionality and machine learning can take advantage of feature selection, which consists of selecting the relevant features and discarding irrelevant ones with a minimum degradation in performance. In this chapter, we will review the most up-to-date feature selection methods, focusing on their scalability properties. Moreover, we will show how these learning methods are enhanced when applied to large scale datasets and, finally, some examples of the application of feature selection in real world databases will be shown.

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