Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

Table 15

Family of hypotheses ordered by the value and adjusting α by Nemenyi and Holm’s procedures, considering an initial α = 0.05.

S. No.	Algo versus algo		z		NM (0.05)	Holm	Ri − Rj	CD
1	RF	KNN	14.8740	6.07E − 08	0.001	0.0011	3.7143	>
2	RBF	KNN	12.9232	2.04E − 07	0.001	0.0011	3.2271	>
3	MLP	KNN	12.2997	3.12E − 07	0.001	0.0012	3.0714	>
4	CDT	KNN	12.2539	3.22E − 07	0.001	0.0012	3.0600	>
5	SVM	KNN	11.3958	5.97E − 07	0.001	0.0012	2.8457	>
6	RF	NB	11.0125	7.97E − 07	0.001	0.0013	2.7500	>
7	J48	RF	10.2001	1.52E − 06	0.001	0.0013	2.5471	>
8	RF	HMM	9.1990	3.57E − 06	0.001	0.0013	2.2971	>
9	RBF	NB	9.0617	4.04E − 06	0.001	0.0014	2.2629	>
10	MLP	NB	8.4381	7.21E − 06	0.001	0.0014	2.1071	>
11	CDT	NB	8.3924	7.53E − 06	0.001	0.0014	2.0957	>
12	J48	RBF	8.2494	8.65E − 06	0.001	0.0015	2.0600	>
13	J48	MLP	7.6258	1.62E − 05	0.001	0.0015	1.9043	>
14	J48	CDT	7.5800	1.7E − 05	0.001	0.0016	1.8929	>
15	A1DE	KNN	7.5800	1.7E − 05	0.001	0.0016	1.8929	>
16	SVM	NB	7.5343	1.78E − 05	0.001	0.0017	1.8814	>
17	RF	A1DE	7.2940	2.3E − 05	0.001	0.0017	1.8214	>
18	RBF	HMM	7.2482	2.41E − 05	0.001	0.0018	1.8100	>
19	SVM	J48	6.7219	4.32E − 05	0.001	0.0019	1.6786	>
20	MLP	HMM	6.6247	4.83E − 05	0.001	0.0019	1.6543	>
21	HMM	CDT	6.5789	5.09E − 05	0.001	0.0020	1.6429	>
22	SVM	HMM	5.7208	0.000143	0.001	0.0021	1.4286	>
23	HMM	KNN	5.6750	0.000152	0.001	0.0022	1.4171	>
24	RBF	A1DE	5.3432	0.000233	0.001	0.0023	1.3343	>
25	MLP	A1DE	4.7196	0.000545	0.001	0.0024	1.1786	>
26	J48	KNN	4.6739	0.000581	0.001	0.0025	1.1671	>
27	CDT	A1DE	4.6739	0.000581	0.001	0.0026	1.1671	>
28	NB	KNN	3.8615	0.001919	0.001	0.0028	0.9643	>
29	SVM	A1DE	3.8158	0.002058	0.001	0.0029	0.9529	>
30	A1DE	NB	3.7185	0.002391	0.001	0.0031	0.9286	>
31	SVM	RF	3.4782	0.003479	0.001	0.0033	0.8686	>
32	J48	A1DE	2.9062	0.00871	0.001	0.0036	0.7257	>
33	RF	CDT	2.6201	0.013903	0.001	0.0038	0.6543	<
34	RF	MLP	2.5743	0.014987	0.001	0.0042	0.6429	<
35	RF	RBF	1.9508	0.041431	0.001	0.0045	0.4871	<
36	HMM	A1DE	1.9050	0.044585	0.001	0.0050	0.4757	<
37	HMM	NB	1.8135	0.051582	0.001	0.0056	0.4529	<
38	SVM	RBF	1.5274	0.080499	0.001	0.0063	0.3814	<
39	J48	HMM	1.0011	0.171458	0.001	0.0071	0.2500	<
40	SVM	MLP	0.9039	0.194805	0.001	0.0083	0.2257	<
41	SVM	CDT	0.8581	0.206548	0.001	0.0100	0.2143	<
42	J48	NB	0.8124	0.218775	0.001	0.0125	0.2029	<
43	RBF	CDT	0.6693	0.260042	0.001	0.0167	0.1671	<
44	MLP	RBF	0.6236	0.274195	0.001	0.0250	0.1557	<
45	MLP	CDT	0.0458	0.482248	0.001	0.0500	0.0114	<