Table 6. Performance of the proposed architectures on other datasets downloaded from UC Irvine Machine Learning Repository (University of California Irvine, 1987), measured through logarithmic loss.

Dataset		Baseline	Semi	Sym	Zero
Breast cancer	Mangasarian, Street & Wolberg (1995)	0.0984	0.0888	0.0966	0.0930
Mammographic	Elter, Schulz-Wendtland & Wittenberg (2007)	0.5122	0.5051	0.4973	0.4822
Parkinson	Little et al. (2007)	0.3945	0.4042	0.3883	0.4323
Pima diabetes	Smith et al. (1988)	0.5269	0.5229	0.5250	0.5472
Lung cancer	Hong & Yang (1991)	1.1083	0.8017	0.6050	0.8328
Cardiotocography	Ayres-de Campos et al. (2000)	0.0113	0.0118	0.0116	0.0110
SPECTF heart	Kurgan et al. (2001)	0.4107	0.4205	0.4121	0.4196
Arcene	Guyon et al. (2005)	1.3516	0.8855	1.0230	1.1518
Colposcopy QA	Fernandes, Cardoso & Fernandes (2017b)	0.5429	0.5406	0.5195	0.4850
Best		1	3	2	3

Note:

We highlight the best performing models in bold.