Classification of Feature Engineering Techniques for Machine Learning under the Environment of Lattice Ordered T-Bipolar Soft Rings-Reference-Cited by-同舟云学术

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Classification of Feature Engineering Techniques for Machine Learning under the Environment of Lattice Ordered T-Bipolar Soft Rings

Published:2024 Issue: Volume:12 Page:77514-77522
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmmad Jabbar¹^ORCID,Labassi Faten²,Alsuraiheed Turki³,Mahmood Tahir¹^ORCID,Ali Khan Meraj²

Affiliation:

1. Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan

2. Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

3. Department of Mathematics, College of Science, King Saud University, Riyadh, Saudi Arabia

Funder

Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10540123.pdf?arnumber=10540123

Reference23 articles.

1. Soft set theory—First results

2. Data analysis approaches of soft sets under incomplete information

3. Multi-attribute decision-making based on soft set theory: a systematic review

4. Soft set theory;Maji;Comput. Math. Appl.,1999

5. On some new operations on soft set theory;Feng;Comput. Math. Appl.,2009

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