Decision Tree Interface for Neural Network Interpretability
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Solution Overview
Problem
Neural networks operate as 'black boxes,' making it difficult to interpret their inner workings and interactions, limiting their interpretability and ease of training, as traditional decision trees and directed acyclic graphs often trade accuracy and automatic feature extraction for enhanced interpretability.
Innovation Solution
Generating and using an interactive decision tree from a neural network, where features such as layers, neurons, weights, biases, and neuron operations are parameterized, allowing users to interact with and modify the neural network through the decision tree, while maintaining accuracy and feature extraction capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional decision trees and directed acyclic graphs are used to enhance interpretability, then interpretability is improved, but accuracy and automatic feature extraction deteriorate
Solution Approach 1:
The patent segments the neural network into hierarchical layers and represents them as structured nodes in a decision tree. Each layer's neurons, weights, and biases are broken down into parameterizable components that can be individually inspected and modified, enabling interpretability without sacrificing the network's computational accuracy and feature extraction capabilities.
Solution Approach 2:
The patent introduces a decision tree as an intermediary interface between the user and the neural network. This intermediary structure allows users to interact with and understand the complex neural network through a more interpretable tree format, while the underlying neural network maintains its full accuracy and automatic feature extraction abilities.
2Reliability
If neural networks are used for high accuracy and automatic feature extraction, then reliability is improved, but interpretability deteriorates due to black box operation
Solution Approach 1:
The decision tree serves as an intermediary that bridges the gap between the black box neural network and user understanding. It provides a structured representation of the network's layers, neurons, and parameters that is interpretable while the neural network itself continues to operate with full accuracy and automatic feature extraction.
Solution Approach 2:
The patent creates a simplified copy of the neural network in the form of a decision tree structure. This copy captures the essential hierarchical organization and parameter relationships without requiring the full computational complexity, allowing users to inspect and understand the network's structure and interactions.
3Ease of operation
If the neural network structure is made interactive through decision tree interface, then ease of operation is improved, but device complexity increases due to parameterization and integration requirements
Solution Approach 1:
The system segments the neural network parameters into hierarchical groups (layers, neurons, weights, biases) that can be independently parameterized and modified through the decision tree interface. This segmentation makes the network easier to operate and train by allowing targeted adjustments to specific components without requiring complex global modifications.
Solution Approach 2:
The decision tree interface serves multiple functions: it provides visual interpretation of the network structure, enables interactive parameter modification, and maintains connectivity to the underlying neural network. This multi-functionality improves ease of operation while the systematic parameterization approach manages the inherent complexity through organized, reusable components.
Data Source
AI summary
A first set of features associated with a neural network are parameterized. A decision tree is generated from the first set of features. One or more adjustments for the neural network are received at the decision tree. A second set of features associated with the adjustments at the decision tree are parameterized. The parameterized first and second set of features are combined into a plurality of parameters. From the plurality, an adjusted neural network is generated.