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

Table 10 Comparison with other multiple classifier system methods

	Methods	Accuracy (%)	Recall	Precision	F-measure	AUC
Dataset 1	Saha et al. [16]	91.85	0.8806	0.8963	0.8872	0.9340
	Chowdhury et al. [26]	95.32	0.9315	0.9414	0.9342	0.9622
	Ghojeski et al. [27] Random subspace	95.05	0.9272	0.9365	0.9303	0.9599
	Ghojeski et al. [27] Bagging	95.71	0.9381	0.9383	0.9378	0.9660
	Proposed method	97.89	0.9718	0.9717	0.9717	0.9844
Dataset 2	Saha et al. [16]	77.52	0.7749	0.7769	0.7718	0.8714
	Chowdhury et al. [26]	92.28	0.9227	0.9239	0.9224	0.9559
	Ghojeski et al. [27] Random subspace	87.52	0.8750	0.8770	0.8746	0.9286
	Ghojeski et al. [27] Bagging	86.26	0.8623	0.8643	0.8620	0.9213
	Proposed method	99.18	0.9892	0.9924	0.9908	0.9940