Table 5. Performance of the proposed architectures on other datasets downloaded from UC Irvine Machine Learning Repository (University of California Irvine, 1987), measured through the area under the Precision–Recall curve.

Dataset		Baseline	Semi	Sym	Zero
Breast cancer	Mangasarian, Street & Wolberg (1995)	0.9795	0.9864	0.9835	0.9856
Mammography	Elter, Schulz-Wendtland & Wittenberg (2007)	0.8551	0.8539	0.8533	0.8489
Parkinson	Little et al. (2007)	0.9517	0.9526	0.9573	0.9604
Pima diabetes	Smith et al. (1988)	0.7328	0.7262	0.7095	0.7331
Lung cancer	Hong & Yang (1991)	0.7083	0.6042	0.6927	0.8021
Cardiotocography	Ayres-de Campos et al. (2000)	0.9948	0.9948	0.9925	0.9958
SPECTF heart	Kurgan et al. (2001)	0.9470	0.9492	0.9462	0.9463
Arcene	Guyon et al. (2005)	0.8108	0.8433	0.8900	0.8455
Colposcopy QA	Fernandes, Cardoso & Fernandes (2017b)	0.7760	0.8122	0.7961	0.8470
Best		1	2	1	5

Note:

We highlight the best performing models in bold.