Feature Selection Method Based on Differential Correlation Information Entropy-Reference-Cited by-同舟云学术

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Feature Selection Method Based on Differential Correlation Information Entropy

Published:2020-08-01 Issue:2 Volume:52 Page:1339-1358
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Wang Xiujuan,Yan Yixuan^ORCID,Ma Xiaoyue

Funder

Natural Science Foundation of Beijing Municipality

National Key R&D Program of China

Research Project of the Department of Computer Science in BJUT

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-020-10307-7.pdf

Reference32 articles.

1. Ding C, Peng H (2003) Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the 2003 IEEE bioinformatics conference, CSB 2003 pp 523–528. https://doi.org/10.1109/CSB.2003.1227396

2. Soltani M, Shammakhi MH, Khorram S, Sheikhzadeh H (2016) Combined mRMR filter and sparse Bayesian classifier for analysis of gene expression data. In: Proceedings—2016 2nd international conference of signal processing and intelligent systems. ICSPIS 2016 https://doi.org/10.1109/ICSPIS.2016.7869891

3. Hanchuan P, Fuhui L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226. https://doi.org/10.1109/TPAMI.2005.159

4. Gu X, Guo J, Xiao L, Ming T, Li C (2019) A feature selection algorithm based on equal interval division and minimal-redundancy-maximal-relevance. Neural Process Lett. https://doi.org/10.1007/s11063-019-10144-3

5. Zheng K, Wang X (2018) Feature selection method with joint maximal information entropy between features and class. Pattern Recogn 77:20–29. https://doi.org/10.1016/j.patcog.2017.12.008

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