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

Table 7 Performance results of feature selection, feature-level fusion and multi-view stacking on Dataset 2 (the best results obtained at each sensor position are italicized)

Positions	Methods	Accuracy (%)	Recall	Precision	F-measure	Errors	AUC
Ankle	SVM	90.24	0.9021	0.9029	0.9019	0.0976	0.9441
	KNN	98.17	0.9617	0.9818	0.9817	0.0183	0.9896
	J48	96.42	0.9642	0.9643	0.9642	0.0358	0.9795
	LR	96.18	0.9617	0.9619	0.9616	0.0382	0.9781
	Stacking-KNN–J48–KNN	98.50	0.9849	0.9850	0.9849	0.0150	0.9914
	Stacking-LR–KNN–J48–LR	98.46	0.9845	0.9845	0.9845	0.0154	0.9912
	Stacking-LR–KNN–J48–MV–LR–KNN	98.37	0.9837	0.9837	0.9837	0.0163	0.9907
Wrist	SVM	94.31	0.9430	0.9454	0.9431	0.0569	0.9674
	KNN	97.44	0.9743	0.9748	0.9744	0.0256	0.9653
	J48	95.33	0.9534	0.9535	0.9534	0.0467	0.9734
	LR	94.15	0.9415	0.9414	0.9414	0.0585	0.9666
	Stacking-KNN–J48–KNN	97.93	0.9793	0.9794	0.9793	0.0207	0.9882
	Stacking-LR–KNN–J48–LR	97.76	0.9777	0.9777	0.9777	0.0224	0.9872
	Stacking-LR–KNN–J48–MV–LR–KNN	98.05	0.9805	0.9806	0.9805	0.0195	0.9889

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