Friedman average rank sum test results.

<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. In medical data analysis, the large number and complexity of features are often accompanied by redundant or irrelevant features, which not only increase the c...

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Bibliographic Details
Main Author:	Chenyi Zhu (9383370) (author)
Other Authors:	Zhiyi Wang (328693) (author), Yinan Peng (21410787) (author), Wenjun Xiao (180027) (author)
Published:	2025
Subjects:	Evolutionary Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified memory storage behavior crucial preprocessing step collaborative hunting behavior also may lead improves global optimization 12 medical datasets feature selection tasks feature selection task paper also constructs elite search behavior shaped transfer functions medical data analysis excellent generalization ability continuous optimization algorithm average accuracy improvement feature selection problem improve search efficiency irbmo significantly improves feature selection significantly improves medical data generalization ability paper proposes paper designs data mining v2irbmo variant turn affects specifically optimized results show results demonstrate promising directions pattern recognition original red often accompanied model overfitting machine learning large number knn classifier key metrics irbmo include irbmo ), improved red fitness value core mechanisms computational burden comprehensively verify classification accuracy
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Friedman average rank sum test results.

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