A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture-Reference-Cited by-同舟云学术

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A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

Published:2017-01-02 Issue:8 Volume:30 Page:2355-2369
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Faris Hossam,Hassonah Mohammad A.,Al-Zoubi Ala’ M.,Mirjalili Seyedali,Aljarah Ibrahim

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2818-2/fulltext.html

Reference52 articles.

1. Carrizosa E, Martín-Barragán B, Morales DR (2014) A nested heuristic for parameter tuning in support vector machines. Comput Oper Res 43:328–334

2. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

3. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27

4. Chao C-F, Horng M-H (2015) The construction of support vector machine classifier using the firefly algorithm. Comput Intell Neurosci 2015:1–8

5. Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(14):131–156

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