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

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Author(s): Amparo Alonso-Betanzos (University of A Coruña, Spain), Verónica Bolón-Canedo (University of A Coruña, Spain), Diego Fernández-Francos (University of A Coruña, Spain), Iago Porto-Díaz (University of A Coruña, Spain)and Noelia Sánchez-Maroño (University of A Coruña, Spain)
Copyright: 2013
Pages: 26
Source title: Efficiency and Scalability Methods for Computational Intellect
Source Author(s)/Editor(s): Boris Igelnik (BMI Research, Inc., USA)and Jacek M. Zurada (University of Louisville, USA)
DOI: 10.4018/978-1-4666-3942-3.ch001

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Information Science Reference

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

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