Table 2 Evaluation of the suggested feature selection technique (bGGO) in comparison to other competitive techniques.

From: Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification

 

bGGO

bSC

bMVO

bPSO

bWOA

bGWO

bFOA

Average error

0.7743031

0.791503

0.805103

0.825303

0.825103

0.811603

0.823703

Average Select size

0.7271031

0.927103

0.869503

0.927103

1.090503

0.849903

0.961603

Average Fitness

0.8375031

0.853703

0.865103

0.852103

0.859903

0.859803

0.904003

Best Fitness

0.7393031

0.774003

0.768403

0.832403

0.824003

0.837603

0.822703

Worst Fitness

0.8378031

0.840903

0.883503

0.900103

0.900103

0.913803

0.920303

Standard deviation Fitness

0.6598031

0.664503

0.666103

0.663903

0.666103

0.665103

0.700703

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