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Table 11 The obtained results for G-mean from boosting algorithms using decision tree as a base learner

From: Boosting methods for multi-class imbalanced data classification: an experimental review

Dataset

Algorithm

AdaBoost.MH

SAMME

CatBoost

LogitBoost

GradientBoost

XGBoost

MEBoost

SMOTEBoost

RUSBoost

LightGBM

AdaC1

AdaC2

AdaC3

AdaCost

Wine

0.9058

0.9149

0.9768

0.9565

0.9908

0.9665

0.9832

0.9501

0.962

0.9688

0.9325

0.9193

0.8776

0.9347

± 0.0170

± 0.0125

± 0.0058

± 0.0102

± 0.0026

± 0.1160

± 0.0077

± 0.0165

± 0.0091

± 0.0138

± 0.0173

± 0.0171

± 0.0133

± 0.0132

Hayes-Roth

0.7948

0.7829

0.7558

0.7901

0.7915

0.786

0.7896

0.7823

0.7793

0.3203

0.7896

0.8003

0.8059

0.7982

± 0.0179

± 0.0116

± 0.0129

± 0.0110

± 0.0162

± 0.0156

± 0.0194

± 0.0164

± 0.0225

± 0.1187

± 0.0127

± 0.0129

± 0.0225

± 0.0204

Contraceptive

0.5243

0.4841

0.5052

0.4819

0.4849

0.5335

0.4971

0.4704

0.4327

0.4926

0.5314

0.1711

0.3828

0.539

± 0.0048

± 0.0079

± 0.0055

± 0.0085

± 0.0101

± 0.0080

± 0.0063

± 0.0126

± 0.0081

± 0.0064

± 0.0051

± 0.0240

± 0.0129

± 0.0088

Pen-based

0.9006

0.9576

0.9615

0.9552

0.9509

0.932

0.9724

0.9496

0.9586

0.9524

0.9031

0.9035

0.9023

0.9027

± 0.0057

± 0.0056

± 0.0046

± 0.0051

± 0.0031

± 0.0055

± 0.0038

± 0.0027

± 0.0048

± 0.0020

± 0.0101

± 0.0053

± 0.0063

± 0.0081

Vertebral column

0.7678

0.7669

0.7921

0.7696

0.7704

0.7733

0.7713

0.7907

0.7765

0.7835

0.7815

0.7801

0.7674

0.7318

± 0.0217

± 0.0239

± 0.0245

± 0.0163

± 0.0193

± 0.0159

± 0.0191

± 0.0239

± 0.0215

± 0.0252

± 0.0113

± 0.0166

± 0.0146

± 0.0184

New thyroid

0.8971

0.8904

0.9099

0.9131

0.9244

0.9065

0.2727

0.9055

0.9131

0.9156

0.8979

0.8869

0.8389

0.8852

± 0.0176

± 0.02311

± 0.0106

± 0.0140

± 0.0098

± 0.0176

± 0.01659

± 0.0240

± 0.01361

± 0.0091

± 0.0268

± 0.0327

± 0.0322

± 0.0238

Dermatology

0.9555

0.9625

0.9786

0.9665

0.9713

0.9693

0.8723

0.9653

0.9433

0.9000

0.9566

0.9602

0.9522

0.9546

± 0.0067

± 0.0040

± 0.0039

± 0.0096

± 0.0050

± 0.0125

± 0.0854

± 0.0061

± 0.0109

± 0.0097

± 0.0070

± 0.0065

± 0.0103

± 0.0075

Balance scale

0.2461

0.4344

0.2898

0.2381

0.0378

0.0098

0.2856

0.4161

0.4877

0.6372

0.073

0.5198

0.4031

0.5881

± 0.0632

± 0.0731

± 0.1676

± 0.0515

± 0.0650

± 0.0310

± 0.0730

± 0.0544

± 0.1068

± 0.0641

± 0.0521

± 0.0074

± 0.0447

± 0.0196

Glass

0.3097

0.4095

0.4591

0.5517

0.3131

0.4252

0.2413

0.2414

0.2014

0.0000

0.3252

0.3382

0.2782

0.3474

± 0.1445

± 0.0571

± 0.1209

± 0.1101

± 0.1880

± 0.1218

± 0.0957

± 0.1493

± 0.1506

± 0.0000

± 0.1040

± 0.1197

± 0.1381

± 0.1161

Heart

0.0000

0.0000

0.0119

0.0000

0.0000

0.0000

0.0000

0.0993

0.0058

0.0000

0.0173

0.0000

0.0307

0.0066

± 0.0000

± 0.0000

± 0.0250

± 0.0000

± 0.0000

± 0.0000

± 0.0000

± 0.0801

± 0.0185

± 0.0000

± 0.0282

± 0.0000

± 0.0324

± 0.0210

Car evaluation

0.9878

0.9474

0.9861

0.9967

0.9852

0.8401

0.9920

0.9304

0.8219

0.9882

0.603

0.8337

0.8852

0.8686

± 0.0091

± 0.0065

± 0.0045

± 0.0036

± 0.0081

± 0.0154

± 0.0042

± 0.0072

± 0.0202

± 0.0036

± 0.0377

± 0.0060

± 0.0089

± 0.0103

Thyroid

0.9771

0.9779

0.9809

0.9767

0.974

0.9789

0.9688

0.9826

0.9517

0.8551

0.0000

0.9827

0.9796

0.0041

± 0.0031

± 0.0017

± 0.0036

± 0.0014

± 0.0025

± 0.0014

± 0.0070

± 0.0020

± 0.0121

± 0.0052

± 0.0000

± 0.0031

± 0.0014

± 0.0129

Yeast

0.0236

0.0000

0.0000

0.0193

0.0000

0.0000

0.0000

0.0513

0.0000

0.0000

0.0000

0.1649

0.0937

0.155

± 0.0499

± 0.0000

± 0.0000

± 0.0408

± 0.0000

± 0.0000

± 0.0000

± 0.0543

± 0.0000

± 0.0000

± 0.0000

± 0.0957

± 0.0929

± 0.0766

Page blocks

0.8275

0.8142

0.8593

0.8302

0.1030

0.8411

0.8290

0.8180

0.6992

0.0473

0.0000

0.3131

0.7762

0.0000

± 0.0119

± 0.0131

± 0.0080

± 0.0093

± 0.0851

± 0.0173

± 0.0118

± 0.0163

± 0.0351

± 0.0035

± 0.0000

± 0.0283

± 0.0095

± 0.0000

Shuttle

0.9195

0.9422

0.8999

0.9707

0.0028

0.9446

0.3690

0.9881

0.8915

0.0000

0.9400

0.9039

0.9205

0.9432

± 0.0766

± 0.0826

± 0.0931

± 0.0165

± 0.0064

± 0.0805

± 0.0521

± 0.0088

± 0.0080

± 0.0000

± 0.0577

± 0.0871

± 0.0726

± 0.0591

Average

0.6691

0.6857

0.6911

0.6944

0.5533

0.6605

0.5896

0.6894

0.6550

0.5241

0.5167

0.6318

0.6596

0.5773

  1. The best performance is shown in italic for each dataset