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

Table 10

Performance evaluation metrics on the Aruba dataset with activities balancing using leave-one-day-out cross-validation.

Dataset	Cross-validation	Clustering method	Classification method	Precision (%)	Recall (%)	F score [0, 1]	Accuracy (%)
Aruba	Leave one day out	Fuzzy C-means [35]	ANN [45]	94.30	94.10	0.94	94.40
			ET-KNN [47]	88.20	88.50	0.88	88.10
			KNN [48]	86.10	86.20	0.86	86.10
			SMO [46]	81.40	81.20	0.81	81.40
		Hierarchical [42]	ANN	89.40	90.20	0.89	89.60
			ET-KNN	87.30	87.80	0.87	87.40
			KNN	85.50	85.10	0.85	85.10
			SMO	81.30	81.30	0.81	81.40
		K-mean [36]	ANN	87.60	87.10	0.87	87.20
			ET-KNN	84.80	85.40	0.85	85.10
			KNN	82.10	81.30	0.81	81.10
			SMO	77.30	77.20	0.77	77.20
		DBSCAN [37]	ANN	85.70	85.10	0.85	85.10
			ET-KNN	81.20	81.30	0.81	81.30
			KNN	79.10	79.10	0.79	79.50
			SMO	78.02	78.20	0.78	78.30

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.