A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data-Reference-Cited by-同舟云学术

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Xie Weidong¹,Li Wei²,Fang Yushan¹,Chi Yuhuan¹,Yu Kun³

Affiliation:

1. Northeastern University,Computer Science and Engineering,Shenyang,China

2. Northeastern University,Key Laboratory of Intelligent Computing in Medical Image,Shenyang,China

3. Northeastern University,Medicine and Bioinformation Engineering,Shenyang,China

Funder

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Liaoning Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995617.pdf?arnumber=9995617

Reference28 articles.

1. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

2. GENEmops: Supervised feature selection from high dimensional biomedical dataset

3. FSelector: a Ruby gem for feature selection

4. A wrapper-filter feature selection technique based on ant colony optimization

5. Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

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