Table 3 Performance evaluation of rank aggregation feature ensemble feature selection with different classifiers.

Dataset	Classifiers	Acy	Pre	Rec	F-1	Spe	BAcy
D₁	SVM	93.10	96.23	92.57	94.36	93.97	93.27
	DT	92.76	95.87	92.37	94.09	93.42	92.89
	KNN	86.98	89.24	88.60	88.92	84.63	86.62
	LR	87.90	91.94	88.58	90.23	86.74	87.66
	RF	90.83	93.93	90.98	92.43	90.58	90.78
	ELM	91.97	95.50	91.74	93.58	92.36	92.05
	GBoost	92.64	96.44	92.15	94.25	93.58	92.86
	XGBoost	93.44	95.38	93.78	94.58	92.90	93.34
	AdaBoost	92.66	95.87	92.21	94.00	93.40	92.80
D₂	SVM	91.16	91.66	93.26	92.45	88.24	90.75
	DT	90.75	92.64	90.98	91.80	90.45	90.72
	KNN	83.99	86.43	85.27	85.84	82.31	83.79
	LR	85.82	90.17	85.83	87.95	85.81	85.82
	RF	92.33	93.60	92.84	93.22	91.65	92.25
	ELM	92.12	93.05	93.29	93.17	90.54	91.92
	GBoost	91.97	93.46	93.15	93.31	90.20	91.68
	XGBoost	92.02	94.17	92.68	93.42	90.98	91.83
	AdaBoost	92.23	93.93	93.05	93.49	90.99	92.02
D₃	SVM	92.40	95.88	93.98	94.92	87.50	90.74
	DT	92.30	95.54	94.14	94.84	86.75	90.44
	KNN	91.45	95.24	93.27	94.24	85.97	89.62
	LR	90.50	95.40	92.20	93.77	84.63	88.42
	RF	92.50	95.87	94.09	94.97	87.65	90.87
	ELM	92.20	95.17	94.41	94.79	85.54	89.97
	GBoost	92.45	95.89	94.35	95.11	85.75	90.05
	XGBoost	92.95	96.13	94.84	95.48	86.01	90.43
	AdaBoost	93.15	96.18	95.02	95.60	86.44	90.73

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