Skip to main content

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