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

Table 9

Performance evaluation metrics on the Aruba dataset with activities balancing using threefold cross-validation.

Dataset	Cross-validation	Clustering method	Classification method	Precision (%)	Recall (%)	F score [0, 1]	Accuracy (%)
Aruba	Threefolds	Fuzzy C-means [35]	ANN [45]	93.60	92.40	0.93	93.30
			ET-KNN [47]	87.30	87.80	0.87	87.40
			KNN [48]	85.20	85.40	0.85	85.10
			SMO [46]	80.80	80.70	0.8	80.30
		Hierarchical [42]	ANN	88.20	88.80	0.88	88.50
			ET-KNN	85.20	85.30	0.85	85.30
			KNN	83.30	83.80	0.83	83.80
			SMO	79.50	79.50	0.79	79.20
		K-mean [36]	ANN	86.20	86.30	0.86	86.70
			ET-KNN	83.20	83.80	0.83	83.80
			KNN	80.40	80.30	0.80	80.20
			SMO	77.30	76.02	0.75	76.02
		DBSCAN [37]	ANN	83.30	83.80	0.83	83.80
			ET-KNN	80.20	80.40	0.80	80.20
			KNN	77.30	77.20	0.77	77.20
			SMO	76.02	76.02	0.76	76.10

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.