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Table 6 Classification results on TCGA datasets (± variance)

From: Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Datasets

Evaluations

CE (L = 1000)

EHP (L = 1000)

CEHP (L = 2000)

CEHPC2 (L = 2000)

RELM

Acc

0.9638 ± 0.0012

0.9589 ± 0.0003

0.9545 ± 0.0012

0.9623 ± 0.0010

Pre

0.9714 ± 0.0003

0.9604 ± 0.0005

0.9434 ± 0.0010

0.9564 ± 0.0016

Recall

0.9846 ± 0.0002

0.9508 ± 0.0003

0.9510 ± 0.0009

0.9584 ± 0.0015

F-mea

0.9778 ± 0.0001

0.9546 ± 0.0004

0.9511 ± 0.0010

0.9564 ± 0.0018

L21-RFELM

Acc

0.9783 ± 0.0005

0.9571 ± 0.0001

0.9547 ± 0.0001

0.9753 ± 0.0001

Pre

0.9714 ± 0.0012

0.9535 ± 0.0001

0.9668 ± 0.0005

0.9747 ± 0.0002

Recall

0.9905 ± 0.0000

0.9426 ± 0.0004

0.9648 ± 0.0005

0.9751 ± 0.0002

F-mea

0.9805 ± 0.0004

0.9478 ± 0.0003

0.9642 ± 0.0005

0.9745 ± 0.0002

LR21ELM

Acc

0.9823 ± 0.0004

0.9383 ± 0.0005

0.9403 ± 0.0002

0.9667 ± 0.0002

Pre

0.9878 ± 0.0004

0.9393 ± 0.0005

0.9422 ± 0.0002

0.9718 ± 0.0000

Recall

0.9877 ± 0.0004

0.9383 ± 0.0007

0.9292 ± 0.0004

0.9605 ± 0.0005

F-mea

0.9865 ± 0.0003

0.9255 ± 0.0010

0.9273 ± 0.0004

0.9653 ± 0.0002

CELM

Acc

0.9062 ± 0.0046

0.9741 ± 0.0000

0.9646 ± 0.0000

0.9767 ± 0.0001

Pre

0.9068 ± 0.0026

0.9691 ± 0.0000

0.9722 ± 0.0000

0.9706 ± 0.0000

Recall

0.9454 ± 0.0042

0.9704 ± 0.0000

0.9690 ± 0.0000

0.9857 ± 0.0001

F-mea

0.9306 ± 0.0027

0.9796 ± 0.0000

0.9707 ± 0.0000

0.9778 ± 0.0000

CSRGELM

Acc

0.9964 ± 0.0001

0.9834 ± 0.0001

0.9709 ± 0.0000

0.9782 ± 0.0001

Pre

0.9956 ± 0.0002

0.9860 ± 0.0000

0.9678 ± 0.0000

0.9813 ± 0.0001

Recall

0.9970 ± 0.0002

0.9741 ± 0.0000

0.9695 ± 0.0000

0.9774 ± 0.0003

F-mea

0.9963 ± 0.0001

0.9796 ± 0.0002

0.9685 ± 0.0000

0.9790 ± 0.0002