Table 4.

Performance evaluation of six encoding methods with state-of-the-art model on training benchmark dataset for F. vesca and R. chinensis species.

Dataset	Method	MCC	ACC	Sn	Sp	AUC
Fragaria Vesca	DNC	0.829	0.914	0.926	0.903	0.96
	TNC	0.825	0.912	0.916	0.909	0.96
	NCP	0.797	0.898	0.922	0.874	0.95
	BE	0.760	0.879	0.905	0.854	094
	NCPNF	0.782	0.891	0.907	0.874	0.95
	MMI	0.659	0.829	0.864	0.794	0.90
	i4mC-ROSE	0.545	0.767	0.635	0.899	0.88
Rosa Chinensis	DNC	0.828	0.914	0.919	0.910	0.96
	TNC	0.811	0.906	0.906	0.906	0.96
	NCP	0.811	0.906	0.901	0.910	0.96
	BE	0.805	0.903	0.891	0.914	0.95
	NCPNF	0.794	0.897	0.892	0.901	0.95
	MMI	0.691	0.846	0.833	0.858	0.92
	i4mC-ROSE	0.563	0.784	0.668	0.900	0.89