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Table 3 Experiment results on 5 different models including 11 performance evaluation metrics

From: Application of multi-label classification models for the diagnosis of diabetic complications

Metric

Traditional model

The MLC models

BR

LP

CC

ECC

CLR

Example-based Metrics

     

 Hamming loss

0.1864

0.2141

0.176

0.176

0.1763

 Accuracy

0.6816

0.6364

0.6948

0.702

0.6875

 F1_score

0.7661

0.721

0.7763

0.7855

0.7711

 Precision

0.8163

0.7706

0.8298

0.8649

0.8156

 Recall

0.78

0.7406

0.7861

0.7727

0.792

Label-based Metrics

     

 F1_micro

0.789

0.7559

0.8000

0.8078

0.7968

 F1_macro

0.7593

0.708

0.7714

0.7773

0.7631

 Precision_micro

0.7709

0.7396

0.7757

0.7592

0.7863

 Precision_macro

0.7884

0.6822

0.7964

0.7689

0.8073

 Recall_micro

0.806

0.7731

0.8261

0.8631

0.8077

 Recall_macro

0.7476

0.7386

0.764

0.8009

0.7394

  1. The MLC models included a Label Power Set (LP), Classifier Chains (CC), Ensemble Classifier Chains (ECC), and Calibrated Label Ranking (CLR). The traditional model Binary Relevance (BR) is used as a comparison. The best performing method is in bold
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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