Comparison of classification error rates for ablation experiments, where the top-ranked results are shown in bold.

Comparison of classification error rates for ablation experiments, where the top-ranked results are shown in bold.

<p>Comparison of classification error rates for ablation experiments, where the top-ranked results are shown in bold.</p>

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Bibliographic Details
Main Author: Genliang Li (5816264) (author)
Other Authors: Yaxin Cui (16850040) (author), Jingyu Su (2522416) (author)
Published: 2025
Subjects:
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
minimal parameter requirements
grey wolf optimizer
enhance search efficiency
prevent premature convergence
feature subset size
global search capability
heuristic algorithm rooted
dimensional classification problems
gwo &# 8217
proposed amgwo method
dimensional classification
search process
global optimum
feature selection
fast convergence
&# 160
classification accuracy
widely used
thus confirming
thereby preventing
thereby enhancing
swarm intelligence
potential solutions
original gwo
machine learning
local optima
known meta
irrelevant features
getting trapped
exploitation effectively
execution speed
eliminate redundant
effectively find
data mining
crucial component
converging prematurely
balance exploration
approach encompasses
allowing amgwo
adaptive mechanism
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