Table 2 Predictive performance of the seven different multi-label algorithms based on a variety of measures

Hamming Loss

0.1943

0.1964

0.1849

0.1953

0.1930

0.2616

0.2064

Accuracy

0.5185

0.5887

0.5876

0.5293

0.5271

0.3427

0.5626

Precision

0.6677

0.6840

0.7071

0.6895

0.6649

0.5184

0.6457

Recall

0.5938

0.7065

0.6962

0.6004

0.6142

0.3802

0.7234

F ₁

0.6278

0.6945

0.7009

0.6411

0.6378

0.4379

0.6814

Subset acc.

0.2759

0.3511

0.3395

0.2839

0.2830

0.1315

0.2869

Micro prec.

0.7351

0.6760

0.7081

0.7280

0.7270

0.6366

0.6541

Micro rec.

0.5890

0.7101

0.6925

0.5958

0.6103

0.3803

0.7189

Micro F₁

0.6526

0.6921

0.6993

0.6540

0.6622

0.4741

0.6840

Micro AUC

0.7465

0.8052

0.8241

0.7475

0.8529

0.7540

0.8474

Macro prec.

0.6877

0.6727

0.7059

0.6349

0.7036

0.4608

0.6535

Macro rec.

0.5707

0.7018

0.6765

0.5722

0.5933

0.3471

0.7060

Macro F₁

0.6001

0.6782

0.6768

0.5881

0.6212

0.3716

0.6681

Macro AUC

0.7343

0.8161

0.8115

0.7317

0.8374

0.7185

0.8344

One-error

0.3038

0.3389

0.2593

0.2964

0.2512

0.3894

0.2946

Coverage

2.4378

1.9300

1.9983

2.4482

1.6914

2.2715

1.7664

Ranking loss

0.2776

0.1867

0.1902

0.2770

0.1456

0.2603

0.1635

Avg. precis.

0.7378

0.7632

0.7983

0.7392

0.8167

0.7104

0.7961