Journal
NEUROCOMPUTING
Volume 300, Issue -, Pages 70-79Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.11.077
Keywords
Feature selection; Dimensionality reduction; Machine learning; Data mining
Categories
Funding
- National Science Foundation of China [61472467, 61672011, 61572180]
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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 frequentlyused evaluation measures for feature selection, and then survey supervised, unsupervised, and semisupervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.
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