Application of Soft Set Theory for Dimensionality Reduction Approach in Machine Learning-Reference-Cited by-同舟云学术

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Application of Soft Set Theory for Dimensionality Reduction Approach in Machine Learning

Published:2021-10-20 Issue: Volume: Page:225-232
ISSN:2367-3370
Container-title:Mathematical, Computational Intelligence and Engineering Approaches for Tourism, Agriculture and Healthcare
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Author:

Lanjewar P. D.,Momin B. F.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-3807-7_17

Reference16 articles.

1. Molodtsov, D.: Soft set theory—first results. Comput. Math. Appl. 37(4–5), 19–31 (1999)

2. Maji, P.K., Roy, A.R., Biswas, R.: An application of soft sets in a decision making problem. Comput. Math. Appl. 44(8–9), 1077–1083 (2002)

3. Chen, D., Tsang, E.C.C., Yeung, D.S., Wang, X.: The parameterization reduction of soft sets and its applications. Int. J. Comput. Math. with Appl. 49, 757–763 (2005)

4. Kong, Z., Liqun, G., Lifu, W., Steven, L.: The normal parameter reduction of soft sets and its algorithm. Comput. Math. Appl. 56, 3029–3037 (2008)

5. Herawan, T., Rose, A.N.M., Deris, M.M.: Soft Set Theoretic Approach for Dimensionality, pp. 171–178 (2009)

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