EURASIP Journal on Wireless Communications and Networking

Table 1 The classification accuracy of features with 32 × 6, 16 × 6 and 64 × 6

From: Features optimization selection in hidden layers of deep learning based on graph clustering

Classification accuracy	Features with 32 × 6			Features with 16 × 6
Classification accuracy	KNN	ε−N	FC	KNN	ε−N	FC
Random	0.545	0.459	0.496	0.556	0.460	0.487
SPEC	0.698	0.721	0.774	0.658	0.701	0.739
ELasso	0.773	0.759	0.782	0.693	0.734	0.755
LapCLasso	0.744	0.801	0.795	0.711	0.803	0.768
Proposed with RC	0.823	0.861	0.805	0.758	0.847	0.815
Proposed with NC	0.788	0.750	0.840	0.749	0.796	0.812

Control group	KNN		\(\varepsilon - N\)		FC
Auto-encoder with features 64 × 6	0.788		0.761		0.830

Bold represents the best performance

Back to article page