Table 10.

p-value and adjusted p-value for pairwise multiple comparisons of different supervised classification algorithms (Phase-I: Normal vs. Abnormal) using the validation set at $\alpha =0.05.$(Abbreviations: SVM: Support Vector Machine, DT: Decision Tree, KNN: k-Nearest Neighbor, NB: Naïve Bayes, ANN: Artificial Neural Network, STD: Standard Deviation, AUC: Area Under Curve, MCC: Matthews Correlation Coefficient).

i	Algorithms	$z=\frac{R_0-R_i}{\mathit{SE}}$	$p$	Holm	Shaffer	Adjusted p-Value
						$p_{\mathit{Holm}}$	$p_{\mathit{Shaffer}}$
28	NB vs. Majority Voting	5.2372	0.0000	0.0018	0.0018	0.0036	0.0036
27	DT vs. Majority Voting	4.5826	0.0000	0.0019	0.0024	0.0037	0.0048
26	NB vs. ANN	4.0916	0.0000	0.0019	0.0024	0.0038	0.0048
25	SVM (RBF Kernel) vs. Majority Voting	3.5460	0.0004	0.0020	0.0024	0.0040	0.0048
24	DT vs. ANN	3.4369	0.0006	0.0021	0.0024	0.0042	0.0048
23	NB vs. SVM (Linear Kernel)	3.2187	0.0013	0.0022	0.0024	0.0043	0.0048
22	NB vs. SVM (Poly Kernel)	3.0551	0.0023	0.0023	0.0024	0.0045	0.0048
21	NB vs. KNN	3.0005	0.0027	0.0024	0.0024	0.0048	0.0048
20	DT vs. SVM (Linear Kernel)	2.5641	0.0103	0.0025	0.0025	0.0050	0.0063
19	DT vs. SVM (Poly Kernel)	2.4004	0.0164	0.0026	0.0026	0.0053	0.0063
18	SVM (RBF Kernel) vs. ANN	2.4004	0.0164	0.0028	0.0028	0.0056	0.0063
17	DT vs. KNN	2.3458	0.0190	0.0029	0.0029	0.0059	0.0063
16	KNN vs. Majority Voting	2.2367	0.0253	0.0031	0.0031	0.0063	0.0063
15	SVM (Poly Kernel) vs. Majority Voting	2.1822	0.0291	0.0033	0.0033	0.0067	0.0067
14	SVM (Linear Kernel) vs. Majority Voting	2.0185	0.0435	0.0036	0.0036	0.0071	0.0071
13	NB vs. SVM (RBF Kernel)	1.6912	0.0908	0.0038	0.0038	0.0077	0.0077
12	SVM (RBF Kernel) vs. SVM	1.5275	0.1266	0.0042	0.0042	0.0083	0.0083
11	SVM (RBF Kernel) vs. SVM	1.3639	0.1726	0.0045	0.0045	0.0091	0.0091
10	SVM (RBF Kernel) vs. KNN	1.3093	0.1904	0.0050	0.0050	0.0100	0.0100
9	ANN vs. Majority Voting	1.1456	0.2519	0.0056	0.0056	0.0111	0.0111
8	KNN vs. ANN	1.0911	0.2752	0.0063	0.0063	0.0125	0.0125
7	DT vs. SVM (RBF Kernel)	1.0365	0.3000	0.0071	0.0071	0.0143	0.0143
6	SVM (Poly Kernel) vs. ANN	1.0365	0.3000	0.0083	0.0083	0.0167	0.0167
5	SVM (Linear Kernel) vs. ANN	0.8729	0.3827	0.0100	0.0100	0.0200	0.0200
4	NB vs. DT	0.6547	0.5127	0.0125	0.0125	0.0250	0.0250
3	KNN vs. SVM (Linear Kernel)	0.2182	0.8273	0.0167	0.0167	0.0333	0.0333
2	SVM (Linear Kernel) vs. SVM	0.1637	0.8700	0.0250	0.0250	0.0500	0.0500
1	KNN vs. SVM (Poly Kernel)	0.0546	0.9565	0.0500	0.0500	0.1000	0.1000