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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Vydáno v:Neurocomputing (Amsterdam) Ročník 300; s. 70 - 79
Hlavní autoři: Cai, Jie, Luo, Jiawei, Wang, Shulin, Yang, Sheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 26.07.2018
Témata:
Data mining
Dimensionality reduction
Feature selection
Machine learning
Dimensionality reduction
Feature selection
Data mining
Machine learning
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí: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