Parameter Settings for competitive algorithms.

<div><p>Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. The Grey Wolf Optimizer (GWO) is a well-known meta-heuristic algorit...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Genliang Li (5816264) (author)
مؤلفون آخرون:	Yaxin Cui (16850040) (author), Jingyu Su (2522416) (author)
منشور في:	2025
الموضوعات:	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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Parameter Settings for competitive algorithms.

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