Feature Subset Selection for High-Dimensional, Low Sampling Size Data Classification Using Ensemble Feature Selection With a Wrapper-Based Search-Reference-Cited by-同舟云学术

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Feature Subset Selection for High-Dimensional, Low Sampling Size Data Classification Using Ensemble Feature Selection With a Wrapper-Based Search

Published:2024 Issue: Volume:12 Page:62341-62357
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mandal Ashis Kumar¹^ORCID,Nadim MD.¹,Saha Hasi²^ORCID,Sultana Tangina³,Hossain Md. Delowar²^ORCID,Huh Eui-Nam⁴^ORCID

Affiliation:

1. Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada

2. Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

3. Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh

4. Department of Computer Science and Engineering, Kyung Hee University, Yongin, South Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation

Korea Government (MSIT)

Artificial Intelligence Convergence Innovation Human Resources Development, Kyung Hee University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10504829.pdf?arnumber=10504829

Reference63 articles.

1. Diagnosis of Rolling Bearing Based on Classification for High Dimensional Unbalanced Data

2. Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

3. Biomarkers as drug development tools: discovery, validation, qualification and use

4. A machine learning approach for hierarchical classification of software requirements

5. Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

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