An Improved Multi-label Relief Feature Selection Algorithm for Unbalanced Datasets-Reference-Cited by-同舟云学术

An Improved Multi-label Relief Feature Selection Algorithm for Unbalanced Datasets

Published:2017-11-01 Issue: Volume: Page:141-151
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Xie Yonghong,Li Daole,Zhang Dezheng,Shuang Ha

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-69096-4_21

Reference16 articles.

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2. Jia, H., Sleep, E.E.G.: Staging studies based on relief algorithm. Lect. Notes Electr. Eng. 163, 935–940 (2013)

3. Gore, S., Govindaraju, V.: Feature selection using cooperative game theory and relief algorithm recent trends, advances and solutions. Knowledge, information and creativity support systems. Springer International Publishing, USA (2016)

4. Gao, L., Li, T., Yao, L., et al.: Research and application of data mining feature selection based on relief algorithm. J. Softw. 9(2) (2014)

5. Lei, P., Wang, J., et al.: Study of emotion recognition under anxiety based on physiological signals by relief method. Chin. J. Med. Instrum. 3, 186–189 (2014)

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