Feature selection in machine learning: A new perspective

High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, an...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 300; pp. 70 - 79
Main Authors: Cai, Jie, Luo, Jiawei, Wang, Shulin, Yang, Sheng
Format: Journal Article
Language:English
Published: Elsevier B.V 26.07.2018
Subjects:
Data mining
Dimensionality reduction
Feature selection
Machine learning
Dimensionality reduction
Feature selection
Data mining
Machine learning
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2017.11.077