[Retracted] Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification

Table 15

Comparison results of our approach OAR-CbC with the state-of-the-art study.

Dataset	Cross-validation	Data sampling	Approach	Precision (%)	Recall (%)	F1 score [0, 1]	Accuracy (%)
Aruba	Threefold	Over-sampling	OAR-CbC	93.60	92.40	0.93	93.30
		Default sampling	OAR-CbC	84.50	84.70	0.84	84.70
	Tenfold	Under-sampling	OAR-CbC	81.30	81.20	0.81	81.90
			[39]	81.90	79.0	0.79	98.54
	Threefold	Default sampling	[23]	75.10	82.90	0.77	—
			[40]	79.65	76.46	0.75	91.40
			[49]	—	—	0.69	87.55

The precision, recall, and accuracy are in percentages (%), while the range of F score is between [0-1], with 1 being the highest. The highest values are in bold.