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

Table 9 Performance using feature selection and SMOTE algorithms on Dataset 2 (the best results obtained at each sensor position are italicized)

Positions	Methods	Accuracy (%)	Recall	Precision	F-Measure	Errors	AUC
Ankle	SVM	93.67	0.9079	0.9573	0.9241	0.0633	0.9489
	KNN	98.45	0.9780	0.9868	0.9820	0.0155	0.9878
	J48	96.08	0.9537	0.9561	0.9549	0.0392	0.9740
	LR	96.87	0.9642	0.9603	0.9622	0.0313	0.9799
	Stacking-KNN–J48–KNN	99.18	0.9892	0.9924	0.9908	0.0082	0.9940
	Stacking-LR–KNN–J48–LR	99.05	0.9880	0.9898	0.9889	0.0095	0.9933
	Stacking-LR–KNN–J48–MV–LR–KNN	99.13	0.9897	0.9905	0.9901	0.0087	0.9942
Wrist	SVM	94.92	0.9392	0.9427	0.9407	0.0508	0.9658
	KNN	98.18	0.9740	0.9840	0.9787	0.0182	0.9856
	J48	96.74	0.9577	0.9605	0.9590	0.0326	0.9765
	LR	94.59	0.9396	0.9383	0.9389	0.0541	0.9658
	Stacking-KNN–J48–KNN	98.99	0.9864	0.9869	0.9866	0.0101	0.9925
	Stacking-LR–KNN–J48–LR	98.98	0.9846	0.9869	0.9842	0.0120	0.9925
	Stacking-LR–KNN–J48–MV–LR–KNN	99.02	0.9868	0.9871	0.9869	0.0098	0.9927

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