Table 8.

Classification Results of PU, PN, NU, PNU in Robot Setup using SVM, RF and LSTM

Item	Metric	SVM				RF				LSTM
set		PU	PN	NU	PNU	PU	PN	NU	PNU	PU	PN	NU	PNU
$R_{\mathit{env}}$	Accuracy	0.521	0.524	0.397	0.384	0.690	0.963	0.678	0.629	0.750	0.521	0.564	0.319
	Precision	0.580	0.543	0.394	0.242	0.691	0.968	0.708	0.733	0.765	0.524	0.564	0.262
	Recall	0.521	0.524	0.397	0.287	0.690	0.963	0.678	0.537	0.750	0.521	0.564	0.205
	F1-score	0.499	0.477	0.392	0.221	0.688	0.962	0.668	0.516	0.744	0.479	0.564	0.215
	Specificity	0.521	0.524	0.397	0.619	0.690	0.963	0.678	0.782	0.750	0.521	0.564	0.504
	GSP	0.524	0.503	0.394	0.241	0.689	0.964	0.681	0.561	0.751	0.500	0.564	0.224
	GSS	0.440	0.428	0.387	0.149	0.683	0.961	0.655	0.548	0.736	0.435	0.563	0.234
	MCC	0.094	0.064	-0.208	-0.155	0.381	0.930	0.385	0.351	0.515	0.045	0.129	-0.338
$R_{\mathit{ton}}$	Accuracy	0.553	0.414	0.461	0.457	0.762	0.545	0.754	0.589	0.625	0.521	0.667	0.403
	Precision	0.568	0.407	0.456	0.575	0.772	0.592	0.828	0.619	0.697	0.441	0.702	0.378
	Recall	0.553	0.414	0.461	0.312	0.762	0.545	0.754	0.423	0.625	0.521	0.667	0.256
	F1-score	0.546	0.403	0.455	0.334	0.750	0.529	0.715	0.465	0.588	0.457	0.657	0.293
	Specificity	0.553	0.414	0.461	0.649	0.762	0.545	0.754	0.749	0.625	0.521	0.667	0.581
	GSP	0.553	0.407	0.457	0.361	0.758	0.548	0.745	0.492	0.618	0.469	0.670	0.304
	GSS	0.536	0.390	0.448	0.391	0.736	0.509	0.674	0.527	0.550	0.338	0.645	0.335
	MCC	0.121	-0.178	-0.083	-0.025	0.534	0.132	0.554	0.194	0.298	0.045	0.366	-0.190