rule extraction from neural networks

**Rule Extraction from Neural Networks** is the **process of distilling the knowledge embedded in a trained neural network into human-readable IF-THEN rules** — converting opaque neural network decisions into transparent, verifiable logical rules that approximate the network's behavior. **Rule Extraction Approaches** - **Decompositional**: Extract rules from individual neurons/layers (e.g., analyzing hidden unit activation patterns). - **Pedagogical**: Treat the network as a black box and learn rules from its input-output behavior. - **Eclectic**: Combine both approaches — use internal network structure to guide rule learning. - **Decision Trees**: Train a decision tree to mimic the neural network's predictions. **Why It Matters** - **Transparency**: Rules are inherently interpretable — engineers can read, verify, and challenge them. - **Validation**: Extracted rules can be validated against domain knowledge to check if the network learned correct relationships. - **Deployment**: In regulated environments, rules may be required instead of black-box neural networks. **Rule Extraction** is **translating neural networks into logic** — converting opaque learned knowledge into transparent, verifiable decision rules.