A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis-Reference-Cited by-同舟云学术

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A Filter-Based Feature Selection and Ranking Approach to Enhance Genetic Programming for High-Dimensional Data Analysis

Published:2023-07-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Congress on Evolutionary Computation (CEC)
language:
Short-container-title:

Author:

Khorshidi Mohammad Sadegh¹,Yazdani Danial¹,Mańdziuk Jacek²,Nikoo Mohammad Reza³,Gandomi Amir H.¹

Affiliation:

1. University of Technology Sydney,Faculty of Engineering and Information Technology,Sydney,Australia

2. Warsaw University of Technology,Faculty of Mathematics and Information Science,Warszawa,Poland

3. Sultan Qaboos University,Department of Civil and Architectural Engineering,Muscat,Oman

Funder

Australian Government through the Australian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10253662/10253666/10254048.pdf?arnumber=10254048

Reference26 articles.

1. A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

2. Software review: the GPTIPS platform

3. A novel feature selection method considering feature interaction

4. R2CI: Information theoretic-guided feature selection with multiple correlations

5. GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining

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