TABLE 6.

The predictive performance comparison of different methods on the Oryza sativa dataset.

Methods	Accu. (%)	Sen. (%)	Prec. (%)	Spec. (%)	MCC (%)	AUC
DHT + KNN	N/A	89.28 ± 0.78	76.41 ± 1.55	72.44 ± 1.58	68.59 ± 1.17	0.8680 ± 0.8900
DHT + RF	N/A	88.00 ± 1.34	87.30 ± 1.35	87.22 ± 1.16	78.26 ± 1.28	0.9199 ± 0.5800
DHT + DNN	82.60 ± 1.79	95.89 ± 0.91	75.79 ± 2.43	69.31 ± 3.53	67.65 ± 2.98	0.9440 ± 0.5800
FFT + DNN	75.31 ± 1.37	93.34 ± 1.59	68.61 ± 1.03	57.23 ± 2.90	54.26 ± 2.81	0.8760 ± 0.0096
DWT + DNN	81.54 ± 3.05	94.81 ± 0.65	75.10 ± 3.84	68.26 ± 6.61	65.50 ± 4.99	0.9309 ± 0.0052
AC + DNN	66.63 ± 4.48	88.42 ± 4.77	62.02 ± 4.91	45.02 ± 12.49	37.39 ± 5.39	0.7931 ± 0.0126
DCT + DNN	80.95 ± 1.10	96.12 ± 1.15	73.70 ± 1.41	65.64 ± 2.40	64.99 ± 1.97	0.9360 ± 0.0017
Our method	94.02 ± 1.45	93.63 ± 1.08	94.39 ± 2.20	94.43 ± 2.19	88.79 ± 2.61	0.9581 ± 0.0140

DHT: discrete Hilbert transform (Cizek, 1970); KNN: k-nearest neighbors; RF: random forest; FFT: fast Fourier transform; DWT: discrete wavelet transform; AC: auto covariance; DCT: discrete cosine transform.

The bold values in these Tables mean the highest value in every column.