Table 8 Evaluation results of AMD-Neov dataset using different sampling strategies with three metrics across ten classification algorithms.

PSO

AUC

0.681

0.659

0.661

0.662

0.678

0.656

0.694

0.628

0.686

0.672

0.668

FMeasure

0.549

0.557

0.537

0.566

0.545

0.556

0.572

0.559

0.552

0.559

0.555

GMean

0.622

0.628

0.619

0.643

0.626

0.630

0.648

0.637

0.631

0.631

0.632

C. Avg.

0.617

0.615

0.605

0.624

0.616

0.614

0.638

0.608

0.623

0.621

0.618

RU

AUC

0.652

0.627

0.625

0.622

0.635

0.649

0.622

0.619

0.663

0.631

0.635

FMeasure

0.549

0.526

0.524

0.534

0.519

0.531

0.543

0.529

0.561

0.539

0.536

GMean

0.637

0.602

0.601

0.609

0.596

0.615

0.615

0.604

0.636

0.612

0.613

C. Avg.

0.613

0.585

0.583

0.588

0.583

0.598

0.593

0.584

0.620

0.594

0.595

RO

AUC

0.643

0.643

0.646

0.659

0.635

0.655

0.638

0.632

0.660

0.657

0.647

FMeasure

0.507

0.542

0.491

0.516

0.498

0.516

0.521

0.506

0.534

0.531

0.516

GMean

0.602

0.629

0.589

0.610

0.599

0.612

0.612

0.598

0.624

0.623

0.610

C. Avg.

0.584

0.605

0.575

0.595

0.577

0.594

0.590

0.579

0.606

0.603

0.591

Cluster

AUC

0.656

0.624

0.627

0.629

0.625

0.652

0.644

0.594

0.642

0.638

0.633

FMeasure

0.551

0.524

0.502

0.538

0.506

0.521

0.546

0.504

0.536

0.537

0.527

GMean

0.641

0.605

0.587

0.624

0.591

0.610

0.630

0.585

0.620

0.621

0.611

C. Avg.

0.616

0.584

0.572

0.597

0.574

0.594

0.607

0.561

0.599

0.599

0.590