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Table 6 The performance results of Feature selection, feature-level fusion and multi-view stacking on Dataset 1 (the best results obtained at each sensor position are italicized)

From: Multi-sensor fusion based on multiple classifier systems for human activity identification

Positions

Methods

Accuracy (%)

Recall

Precision

F-measure

Errors

AUC

Ankle

SVM

93.08

0.9029

0.9312

0.8987

0.0692

0.9465

KNN

94.67

0.9243

0.9303

0.9272

0.0533

0.9272

J48

95.13

0.9283

0.9380

0.9330

0.0487

0.9603

LR

95.60

0.9430

0.9382

0.9404

0.0440

0.9683

Stacking–KNN–J48–KNN

96.79

0.9592

0.9583

0.9587

0.0321

0.9772

Stacking-LR–KNN–J48–LR

97.49

0.9648

0.9673

0.9660

0.0251

0.9804

Stacking–LR–KNN–J48–MV–LR–KNN

97.57

0.9672

0.9669

0.9670

0.0243

0.9818

Chest

SVM

94.09

0.9107

0.9355

0.9210

0.0591

0.9505

KNN

94.45

0.9148

0.9428

0.9275

0.0545

0.9528

J48

93.35

0.9107

0.9185

0.9145

0.0665

0.9500

LR

95.32

0.9379

0.9373

0.9375

0.0468

0.9654

Stacking-KNN–J48–KNN

95.05

0.9323

0.9405

0.9362

0.0495

0.9621

Stacking-LR–KNN–J48–LR

95.67

0.9398

0.9477

0.9435

0.0433

0.9665

Stacking-LR–KNN–J48–MV–LR–KNN

96.02

0.9425

0.9528

0.9474

0.0398

0.9681

Wrist

SVM

90.96

0.8432

0.9293

0.8758

0.0904

0.9134

KNN

93.32

0.8828

0.9514

0.9111

0.0668

0.9311

J48

91.89

0.8885

0.9020

0.8947

0.0811

0.9376

LR

91.38

0.8780

0.8884

0.8828

0.0862

0.9323

Stacking-KNN–J48–KNN

94.74

0.9209

0.9448

0.9319

0.0526

0.9560

Stacking-LR–KNN–J48–LR

95.32

0.9324

0.9472

0.9395

0.0468

0.9623

Stacking-LR–KNN–J48–MV–LR–KNN

95.48

0.9330

0.9501

0.9412

0.0452

0.9628

  1. Italic values show multiple classifiers combinations with the highest values and produce superior results compared to single classifications and feature-level fusion