Neural Network to Decision Tree Conversion for Interpretability
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Solution Overview
Problem
The black-box nature of neural networks hinders their wider adoption in industries like health and security due to the lack of clear explanations for their decision-making processes, with existing methods providing noisy or incomplete saliency maps that fail to offer detailed logical reasoning.
Innovation Solution
Converting neural networks into decision trees using effective filters as decision rules, allowing for the explanation of neural network decisions without altering the network architecture, and employing lossless pruning to reduce redundancy in the decision tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If saliency maps are obtained via gradient of neural network output with respect to input, then visualization of input-specific linearization is achieved, but the saliency maps are noisy and prevent clear understanding of decisions
Solution Approach 1:
The patent segments the neural network into multiple linear regions based on activation function boundaries. By dividing the network's decision space into distinct linear segments and analyzing each separately, the method achieves clearer saliency maps without the noise that plagues gradient-based approaches applied to the entire network at once.
Solution Approach 2:
Instead of computing saliency across the entire neural network simultaneously, the patent applies partial action by focusing on one linear region at a time. This step-by-step approach through successive linear regions allows for precise local analysis that accumulates into clear global interpretation, avoiding the noise accumulation that occurs with full-network gradient computation.
2Measurement precision
If derivative of neural network output with respect to activation is used, then clearer saliency maps highlighting predicted class areas are obtained, but detailed logical reasoning of why such decision is made is still lacking
Solution Approach 1:
The patent introduces dynamics by transitioning from static saliency maps to a dynamic traversal process through linear regions. The method sequentially visits different linear regions in order of their contribution to the final decision, building up logical reasoning step-by-step. This dynamic approach reveals the sequence and rationale of decision-making, not just the final salient areas.
Solution Approach 2:
The patent performs preliminary action by pre-identifying and ordering the linear regions before generating saliency explanations. By establishing the sequence of linear regions in advance based on their activation thresholds and contributions, the method prepares a structured framework that enables logical reasoning about decision pathways before the actual saliency computation occurs.
3Reliability
If neural network is converted into decision tree covering all effective filters, then interpretability is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential linear regions and their boundary conditions from the complex neural network to construct the decision tree. By taking out and representing only the critical decision boundaries and linear region transitions rather than all possible filter combinations, the method achieves good interpretability while controlling decision tree complexity.
Solution Approach 2:
The patent applies partial action by covering the most important linear regions and decision boundaries rather than exhaustively representing every possible filter and region combination. This selective approach to building the decision tree captures the essential interpretability benefits while avoiding the exponential complexity growth that would result from complete coverage.
Data Source
AI summary
A method for converting neural network, applied to a terminal device, including: initializing a decision tree, and setting a root of the decision tree; and branching leafs from the root of the decision tree based on effective filters of the neutral network as a decision rule, until all effective filters of the neutral network are covered by the decision tree. The neutral network is a piece-wise linearly activated neutral network. In this method, the neutral network is converted as decision trees and is explained based on the decision trees, so as to solve the black-box problem of the neutral network.