Table 3 Evaluation results of Blood dataset using different sampling strategies with three metrics across ten classification algorithms.

PSO

AUC

0.693

0.656

0.706

0.656

0.736

0.612

0.696

0.667

0.660

0.720

0.680

FMeasure

0.495

0.446

0.458

0.430

0.494

0.409

0.485

0.486

0.434

0.487

0.462

GMean

0.671

0.622

0.634

0.605

0.668

0.590

0.662

0.655

0.614

0.663

0.638

C. Avg.

0.620

0.575

0.599

0.564

0.633

0.537

0.614

0.603

0.569

0.623

0.593

RU

AUC

0.663

0.647

0.713

0.632

0.745

0.597

0.689

0.666

0.638

0.710

0.669

FMeasure

0.474

0.425

0.417

0.419

0.511

0.393

0.461

0.486

0.424

0.462

0.447

GMean

0.643

0.609

0.586

0.600

0.686

0.577

0.641

0.655

0.605

0.639

0.624

C. Avg.

0.593

0.560

0.572

0.550

0.647

0.522

0.597

0.602

0.556

0.604

0.580

RO

AUC

0.657

0.635

0.710

0.618

0.749

0.573

0.652

0.671

0.629

0.715

0.661

FMeasure

0.460

0.422

0.375

0.387

0.514

0.339

0.432

0.491

0.380

0.474

0.428

GMean

0.635

0.607

0.538

0.568

0.689

0.522

0.615

0.663

0.561

0.651

0.605

C. Avg.

0.584

0.555

0.541

0.524

0.651

0.478

0.566

0.608

0.523

0.613

0.565

Cluster

AUC

0.616

0.660

0.677

0.614

0.651

0.571

0.661

0.658

0.629

0.711

0.645

FMeasure

0.449

0.420

0.382

0.405

0.429

0.318

0.414

0.419

0.348

0.454

0.404

GMean

0.587

0.583

0.556

0.559

0.560

0.534

0.616

0.635

0.608

0.658

0.590

C. Avg.

0.551

0.554

0.538

0.526

0.547

0.474

0.564

0.571

0.528

0.608

0.546