Multi-sensor fusion based on multiple classifier systems for human activity identification

Table 8 Performance using feature selection and SMOTE algorithm on Dataset 1 (the best results obtained at each sensor position are italicized)

Positions	Methods	Accuracy (%)	Recall	Precision	F-measure	Errors	AUC
Ankle	SVM	93.98	0.9060	0.9299	0.8991	0.0602	0.9488
	KNN	95.33	0.9295	0.9314	0.9303	0.0467	0.9614
	J48	96.45	0.9537	0.9560	0.9547	0.0355	0.9742
	LR	96.48	0.9504	0.9505	0.9502	0.0352	0.9727
	Stacking-KNN–J48–KNN	97.25	0.9629	0.9621	0.9624	0.0275	0.9794
	Stacking-LR–KNN–J48–LR	97.92	0.9709	0.9713	0.9711	0.0208	0.9838
	Stacking-LR–KNN–J48–MV–LR–KNN	97.89	0.9718	0.9717	0.9717	0.0211	0.9844
Chest	SVM	94.43	0.9170	0.9386	0.9254	0.0557	0.9541
	KNN	95.20	0.9307	0.9434	0.9364	0.0480	0.9616
	J48	93.79	0.9197	0.9240	0.9218	0.0621	0.9551
	LR	95.42	0.9419	0.9443	0.9430	0.0458	0.9675
	Stacking-KNN–J48–KNN	95.87	0.9466	0.9465	0.9464	0.0413	0.9702
	Stacking-LR–KNN–J48–LR	96.54	0.9565	0.9586	0.9575	0.0346	0.9756
	Stacking-LR–KNN–J48–MV–LR–KNN	96.73	0.9586	0.9603	0.9594	0.0327	0.9768
Wrist	SVM	91.26	0.8956	0.9254	0.9085	0.0874	0.9407
	KNN	95.30	0.9381	0.9534	0.9451	0.0470	0.9654
	J48	92.39	0.9222	0.9243	0.9232	0.0761	0.9552
	LR	91.72	0.9040	0.9067	0.9052	0.0828	0.9457
	Stacking-KNN–J48–KNN	95.91	0.9504	0.9522	0.9513	0.0409	0.9721
	Stacking-LR–KNN–J48-LR	96.17	0.9524	0.9545	0.9477	0.0383	0.9733
	Stacking-LR–KNN–J48–MV–LR–KNN	96.63	0.9572	0.9616	0.9593	0.0337	0.9760

Italic values show multiple classifiers combinations with the highest values and produce superior results compared to single classifications and feature-level fusion