cs.LOFeb 25, 2026
SaveSlice and Explain: Logic-Based Explanations for Neural Networks through Domain Slicing
Organizations: Instituto Federal do Ceará (IFCE), Maracanaú, Ceará, Brazil
Abstract
Neural networks (NNs) are pervasive across various domains but often lack interpretability. To address the growing need for explanations, logic-based approaches have been proposed to explain predictions made by NNs, offering correctness guarantees. However, scalability remains a concern in these methods. This paper proposes an approach leveraging domain slicing to facilitate explanation generation for NNs. By reducing the complexity of logical constraints through slicing, we decrease explanation time by up to 40% less time, as indicated through comparative experiments. Our findings highlight the efficacy of domain slicing in enhancing explanation efficiency for NNs.
Figures & tables
Figure 1: Example of Neural Network
Figure 2: Slicing respective to subdomain
Figure 3: Slicing respective to subdomain
| Dataset | Metric | 0 slices | 1 slice | 2 slices | 3 slices |
|---|---|---|---|---|---|
| Auto | Exp Time (s) | 12.56 | 13.17 | 13.32 | 13.80 |
| (25) | % Bin Rem | 0.00 | 0.00 | 0.00 | 0.00 |
| Hepatitis | Exp Time (s) | 4.49 | 5.83 | 6.74 | 9.97 |
| (19) | % Bin Rem | 0.00 | 0.00 | 0.00 | 0.00 |
| Australian | Exp Time (s) | 32.20 | 33.25 | 33.01 | 34.09 |
| (14) | % Bin Rem | 0.00 | 1.00 | 1.50 | 2.13 |
Table 1: Explanation time (in seconds) and average % binary variables removed by number of slices for 2-layers NNs.
| Dataset | Metric | 0 slices | 1 slice | 2 slices | 3 slices |
|---|---|---|---|---|---|
| Auto | Exp Time (s) | 56.40 | 48.84 | 68.86 | 51.12 |
| (25) | % Bin Rem | 0.00 | 0.00 | 0.00 | 0.00 |
| Hepatitis | Exp Time (s) | 15.85 | 19.82 | 19.90 | 18.59 |
| (19) | % Bin Rem | 0.00 | 0.00 | 0.00 | 0.38 |
| Australian | Exp Time (s) | 149.92 | 154.09 | 142.43 | 153.18 |
| (14) | % Bin Rem | 0.00 | 11.00 | 12.75 | 16.00 |
Table 2: Explanation time (in seconds) and average % of binary variables removed by number of slices for 3-layers NNs. The values in bold represent the slices that achieved a reduction in explanation time compared to 0 slices.
| Dataset | Metric | 0 slices | 1 slice | 2 slices | 3 slices |
|---|---|---|---|---|---|
| Auto | Exp Time (s) | 387.34 | 454.78 | 466.26 | 460.04 |
| (25) | % Bin Rem | 0.00 | 0.00 | 0.00 | 0.00 |
| Hepatitis | Exp Time (s) | 463.45 | 434.36 | 510.26 | 625.97 |
| (19) | % Bin Rem | 1.00 | 1.00 | 1.50 | 1.50 |
| Australian | Exp Time (s) | 6156.11 | 6017.00 | 5195.69 | 3867.26 |
| (14) | % Bin Rem | 1.00 | 15.50 | 19.00 | 23.38 |
Table 3: Explanation time (in seconds) and average % of binary variables removed by number of slices for 4-layers NNs. The values in bold represent the slices that achieved a reduction in explanation time compared to 0 slices.