ReNN: Rule-embedded Neural Networks
This addresses interpretability and data efficiency issues in neural networks for domains like time-series analysis, but appears incremental as it builds on existing neural network methods with rule integration.
The paper tackles the problem of neural networks lacking interpretability and requiring large datasets by proposing Rule-embedded Neural Networks (ReNN), which integrates domain knowledge rules to improve inference accuracy and reduce data needs, validated on a time-series detection problem.
The artificial neural network shows powerful ability of inference, but it is still criticized for lack of interpretability and prerequisite needs of big dataset. This paper proposes the Rule-embedded Neural Network (ReNN) to overcome the shortages. ReNN first makes local-based inferences to detect local patterns, and then uses rules based on domain knowledge about the local patterns to generate rule-modulated map. After that, ReNN makes global-based inferences that synthesizes the local patterns and the rule-modulated map. To solve the optimization problem caused by rules, we use a two-stage optimization strategy to train the ReNN model. By introducing rules into ReNN, we can strengthen traditional neural networks with long-term dependencies which are difficult to learn with limited empirical dataset, thus improving inference accuracy. The complexity of neural networks can be reduced since long-term dependencies are not modeled with neural connections, and thus the amount of data needed to optimize the neural networks can be reduced. Besides, inferences from ReNN can be analyzed with both local patterns and rules, and thus have better interpretability. In this paper, ReNN has been validated with a time-series detection problem.