Neural Network Rule Extraction via Decision Tree Threshold Optimization
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Solution Overview
Problem
Existing rule extraction algorithms for artificial neural networks suffer from low fidelity due to inaccurate quantization methods, which fail to capture when a filter or neuron is truly active, leading to information loss and errors in explaining the decision-making process of black-box models.
Innovation Solution
A method that trains decision trees to approximate the behavior of neural networks, using filter or neuron activations as input and predictions as output, to find optimal threshold values that minimize information loss and improve the fidelity of rule extraction by selecting threshold values that maximize the user-defined criterion, such as entropy or Gini index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional quantization methods (thresholding, sample statistics) are used to convert float activations to binary values, then the process is simple and fast, but information loss occurs and fidelity of rule extraction deteriorates
Solution Approach 1:
The patent applies preliminary action by training decision trees to approximate the neural network behavior before the actual rule extraction process. The decision trees are trained on filter activations and target outputs, and their threshold values are pre-calculated and stored. This preliminary approximation captures the essential decision boundaries, reducing information loss when activations are later quantized to binary values for rule extraction.
Solution Approach 2:
The patent introduces decision trees as an intermediary between the continuous neural network activations and the binary quantized values used in rule extraction. The decision trees serve as a mediator that approximates the neural network's decision-making process, providing optimized threshold values that minimize information loss during the transition from float to binary representation.
2Measurement precision
If simple thresholding methods are used for quantization, then the rule extraction process is fast and simple, but the fidelity of extracted rules with respect to original model predictions is low
Solution Approach 1:
The patent performs preliminary action by pre-training decision trees to approximate the neural network and pre-calculating optimal threshold values. This preparation work is done once before rule extraction, enabling faster and more accurate quantization during the actual rule extraction process without sacrificing fidelity.
Solution Approach 2:
The patent changes the parameter optimization approach by using decision tree-based threshold selection instead of simple fixed or statistical thresholds. The decision trees optimize threshold parameters to maximize fidelity between extracted rules and original model predictions, while the pre-calculated thresholds enable efficient processing.
3Reliability
If decision trees are trained to approximate neural network behavior with optimized threshold values, then fidelity of rule extraction is improved, but computational complexity and training time increase
Solution Approach 1:
The patent extracts the essential decision-making logic from the neural network by training decision trees to approximate its behavior. The decision trees extract and represent the critical threshold values and decision boundaries, separating the approximation function from the original complex neural network structure.
Solution Approach 2:
The patent creates a simplified copy of the neural network's decision-making process using decision trees. This tree-based copy approximates the neural network's behavior with interpretable rules and optimized thresholds, maintaining reliability while reducing complexity for the rule extraction stage.
Data Source
AI summary
Rules for explaining the output of an ANN are derived by: creating decision trees trained to approximate the ANN and optimize a defined criterion, a threshold value for the criterion being calculated to determine for which node of the ANN the input activations should be split between branches of the decision tree; obtaining threshold value combinations each comprising a threshold value obtained for respective nodes of the ANN; for each combination, using the combination to perform a rule extraction algorithm to extract a rule explaining the output of the ANN and to obtain a fidelity metric indicating the accuracy of the rule with respect to predictions of the ANN; determining which combination yields the best fidelity metric; and using the rule extraction algorithm with the combination of threshold values determined to yield the best fidelity metric to extract at least one rule for explaining the output of the ANN.