Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm-Reference-Cited by-同舟云学术

Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm

Published:2021-09-17 Issue: Volume: Page:367-375
ISSN:2367-3370
Container-title:Proceedings of Sixth International Congress on Information and Communication Technology
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Author:

Ghorpade-Aher Jayshree,Sonkamble Balwant

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-2380-6_32

Reference16 articles.

1. Ding W, Lin C, Pedrycz W (2020) Multiple relevant feature ensemble selection based on multilayer co-evolutionary consensus mapreduce. IEEE Trans Cybern 50(2):425–439

2. Zhao Y, Duangsoithong R (2020) Empirical analysis using feature selection and bootstrap data for small sample size problems. In: IEEE 16th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), Pattaya, Chonburi, Thailand, pp 814–817. (January 2020)

3. Ghorpade J, Sonkamble B (2020) Predictive analysis of heterogeneous data–techniques & tools. In: 2020 5th international conference on computer and communication systems (ICCCS), Shanghai, China, pp 40–44. (May 2020)

4. Pes B (2019) Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. In: Neural computing and applications. Springer pp 1–12. (February 2019)

5. Wang J, Xu J, Zhao C, Peng Y, Wang H (2019) An ensemble feature selection method for high-dimensional data based on sort aggregation. Syst Sci Control Eng IEEE Access 7(2):32–39

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