Interpretable Neural Network Prediction via Condition Data Segmentation
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Solution Overview
Problem
Existing machine learning models, such as neural networks, face challenges in achieving high prediction precision while allowing for easy interpretation of the prediction process, as they often result in complex internal structures that are difficult to understand.
Innovation Solution
An information processing apparatus and method that uses a neural network to generate condition data representing simple conditions satisfied by input data, allowing for the determination of prediction data based on correct answer data associated with example data that satisfy these conditions, thereby providing an interpretable prediction rule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is used for prediction, then prediction precision is improved, but interpretability of the prediction process deteriorates
Solution Approach 1:
The patent segments the neural network's complex inference process into multiple simple, interpretable determination rules. Each rule corresponds to a specific path from root to leaf in the decision tree structure, breaking down the black-box neural network into transparent, human-understandable decision pathways while preserving prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism (decision tree structure with determination rules) that bridges the gap between the neural network's complex internal processing and human interpretability. This intermediary translates neural network outputs into readable if-then rules without sacrificing the high prediction precision of the original neural network.
2Ease of operation
If a graphical model is used instead of neural network, then interpretability is improved, but prediction precision deteriorates
Solution Approach 1:
The patent merges the advantages of both neural networks and graphical models by combining the high prediction precision of neural networks with the interpretability of decision trees. The system uses neural networks to learn optimal determination rules from data, then represents these rules in an interpretable decision tree format, achieving both accuracy and understandability simultaneously.
3Ease of operation
If a decision tree is used for prediction, then interpretability is improved, but prediction precision deteriorates
Solution Approach 1:
The patent changes the parameters and structure of the decision tree by allowing complex, multi-condition determination rules that capture nuanced patterns in the data. Rather than simple binary splits, the system generates comprehensive if-then rules with multiple conditions, enabling the decision tree to achieve high prediction precision while maintaining interpretability through the readable rule format.
Data Source
AI summary
An information processing apparatus (2000) acquires input data (10) and generates, by use of a neural network (30), condition data (50) that indicate one or more conditions satisfied by the input data (10). The information processing apparatus (2000) determines prediction data (20) by use of a value determined based on correct answer data (42) associated with example data (40) that satisfy at least a part of conditions indicated by the condition data (50).