Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information

Table 2 Performance of traditional classifiers on the training set with 10-fold cross-validation

	ACC (%)	SN	SP	MCC	Time	ACC_Std	SN_std	SP_Std	MCC_Std	AUC_Std
GBM	72.13	0.731	0.712	0.442	35.879	0.020	0.030	0.015	0.042	0.018
RF	72.66	0.719	0.736	0.454	44.149	0.021	0.038	0.013	0.041	0.019
SVM	73.06	0.752	0.711	0.463	181.59	0.014	0.029	0.023	0.031	0.014
ERT	72.47	0.731	0.720	0.450	13.433	0.023	0.033	0.018	0.045	0.020
LR	69.71	0.719	0.677	0.396	0.406	0.029	0.039	0.032	0.061	0.029
AdaBoost	67.82	0.689	0.668	0.357	76.94	0.024	0.034	0.025	0.048	0.017
DT	63.87	0.651	0.625	0.277	16.209	0.026	0.026	0.048	0.051	0.025
NB	63.98	0.721	0.559	0.282	2.220	0.020	0.044	0.013	0.042	0.022
XGB	71.43	0.721	0.707	0.428	57.816	0.012	0.015	0.019	0.022	0.018

\({}^{\textrm{ACC}\_\textrm{Std,SN}\_\textrm{std,SP}\_\textrm{Std,MCC}\_\textrm{Std,AUC}\_\textrm{Std}}\) these are the standard deviations of ACC, SN, SP, MCC and AUC when each classifier performs 10-fold cross validation on the training set

ISSN: 1471-2105