Table 6.

Training Specificity, Sensitivity, and F-Measurere.

Dataset/Algorithm		BP	PSOLF	LFPSO	LPSONS
Breast Cancer	Spec	0.9495	0.9587	0.9605	0.9615
	Sens	0.9549	0.9703	0.9735	0.9749
	F-Measure	0.9560	0.9696	0.9729	0.9738
Liver	Spec	0.7331	0.7309	0.7549	0.769
	Sens	0.7089	0.7029	0.7372	0.7423
	F-Measure	0.7327	0.7160	0.7452	0.7526
Pima	Spec	0.6633	0.7495	0.7559	0.7506
	Sens	0.6283	0.7276	0.7373	0.7402
	F-Measure	0.6588	0.7495	0.7511	0.7581
Wine	Spec	0.9777	0.9904	0.9962	1.0000
	Sens	0.9713	0.9852	0.9968	0.9980
	F-Measure	0.9692	0.9837	0.9967	0.9977
Australian	Spec	0.8222	0.8407	0.8598	0.8629
	Sens	0.8604	0.8719	0.8804	0.8807
	F-Measure	0.8691	0.8709	0.8799	0.8805
Hepatitis	Spec	0.9275	0.9694	0.9819	0.9892
	Sens	0.7995	0.9189	0.9465	0.9735
	F-Measure	0.8312	0.9322	0.9556	0.9805
Heart	Spec	0.8778	0.8926	0.8848	0.8970
	Sens	0.8655	0.8676	0.8734	0.8870
	F-Measure	0.8675	0.8715	0.8755	0.8889
Blood	Spec	0.6120	0.6750	0.6726	0.6898
	Sens	0.5363	0.5625	0.5761	0.5944
	F-Measure	0.6205	0.6361	0.6444	0.6609
Iris	Spec	0.9762	0.9392	0.9737	0.9854
	Sens	0.9661	0.9520	0.9665	0.9747
	F-Measure	0.9667	0.9528	0.9670	0.9757
Credit	Spec	0.9147	0.9177	0.9215	0.9236
	Sens	0.8850	0.8840	0.8862	0.8561
	F-Measure	0.8837	0.8827	0.8848	0.8713
Seeds	Spec	0.8670	0.9605	0.9737	0.9728
	Sens	0.9360	0.9475	0.9557	0.9641
	F-Measure	0.9395	0.9484	0.9567	0.9643
Haberman	Spec	0.6138	0.6296	0.6483	0.6245
	Sens	0.5717	0.5854	0.5959	0.5845
	F-Measure	0.6325	0.6373	0.6491	0.6377
Balance	Spec	0.7922	0.8325	0.8884	0.9018
	Sens	0.6307	0.6372	0.6417	0.6448
	F-Measure	0.6769	0.6801	0.6981	0.7013
Diabetes	Spec	0.6436	0.6925	0.6711	0.6675
	Sens	0.6835	0.7096	0.7230	0.7137
	F-Measure	0.6908	0.7071	0.7217	0.7135