Gene selection with Game Shapley Harris hawks optimizer for cancer classification-Reference-Cited by-同舟云学术

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Gene selection with Game Shapley Harris hawks optimizer for cancer classification

Published:2023-11 Issue: Volume:242 Page:104989
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Afreen Sana,Bhurjee Ajay Kumar,Aziz Rabia Musheer

Funder

Science and Engineering Research Board

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference57 articles.

1. Machine learning methods for cancer classification using gene expression data: A review;Alharbi;Bioengineering,2023

2. Multiple reference points-based decomposition for multiobjective feature selection in classification: Static and dynamic mechanisms;Nguyen;IEEE Trans. Evol. Comput.,2019

3. A survey on evolutionary algorithms dynamics and its complexity–mutual relations, past, present and future;Zelinka;Swarm Evol. Comput.,2015

4. A review of microarray datasets and applied feature selection methods;Bolón-Canedo;Inf. Sci.,2014

5. Feature evaluation and selection with cooperative game theory;Sun;Pattern Recognit.,2012

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1. Cancer classification using RNA sequencing gene expression data based on Game Shapley local search embedded binary social ski-driver optimization algorithms;Microchemical Journal;2024-10

2. A multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information;Journal of Ambient Intelligence and Humanized Computing;2024-09-12

3. An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders;International Research Journal of Multidisciplinary Technovation;2024-07-30

4. A novel two-stage wrapper feature selection approach based on greedy search for text sentiment classification;Neurocomputing;2024-07

5. Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback;Computers in Biology and Medicine;2024-06

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