Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm-Reference-Cited by-同舟云学术

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Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm

Published:2020-05-21 Issue:3 Volume:12 Page:288-301
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Zhang Ge,Hou Jincui,Wang Jianlin,Yan Chaokun^ORCID,Luo Junwei

Funder

NSFC

Scientific Research Foundation of the Higher Education Institutions of Henan Province

Science and Technology Development Plan Project of Henan Province

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-020-00372-w.pdf

Reference42 articles.

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2. Luo H, Wang J, Li M, Luo J, Ni P, Zhao K, Wu F, Pan Y (2018) Computational drug repositioning with random walk on a heterogeneous network. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TCBB.2018.2832078

3. Zhang C, Cai H, Huang J, Song Y (2016) nbcnv: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data. BMC Bioinform 17(1):384. https://doi.org/10.1186/s12859-016-1239-7

4. Liu H, Zhao, Z (2012) Manipulating data and dimension reduction methods: Feature selection. In: Computational Complexity: theory, techniques, and applications, pp. 1790–1800. Springer, New York. https://doi.org/10.1007/978-1-4614-1800-9_115

5. Ekbal A, Saha S (2015) Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition. Knowl Based Syst 85:37–51. https://doi.org/10.1016/j.knosys.2015.04.015

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