Latent Feature Constraints for Explainable Neural Classifiers
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Solution Overview
Problem
Traditional neural network models lack interpretability, leading to unexplainable decisions, especially in regulated industries, and often overfit to training data, making them unreliable for new data and computationally expensive.
Innovation Solution
Apply regularization constraints to reduce incoming connections to latent features in a neural network, use loss function weighting based on activation tuples, and selectively update weights of top-ranked latent features to enhance interpretability and explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural network models are used for predictive purposes, then predictive accuracy can be achieved, but interpretability and explainability deteriorate
Solution Approach 1:
The patent segments the neural network architecture by introducing a separate explanation layer that independently generates explanations for each prediction. This segmentation allows the predictive model to maintain high accuracy while the explanation layer provides interpretability, resolving the contradiction between predictive performance and explainability.
Solution Approach 2:
The patent introduces an intermediary explanation generation mechanism that acts as a mediator between the predictive model and the user. This intermediary component translates the black-box predictions into human-readable explanations without compromising the underlying predictive accuracy, thus addressing both requirements simultaneously.
2Measurement precision
If neural network models are made more complex to improve predictive performance, then accuracy improves, but computational cost and complexity increase
Solution Approach 1:
The patent segments the computational tasks by separating the predictive processing from the explanation generation. The neural network focuses on accurate prediction while the explanation layer handles interpretability, allowing the model to achieve high accuracy without unnecessarily increasing overall system complexity.
Solution Approach 2:
The patent applies partial action by generating explanations only for the most important predictions or for specific user requests rather than continuously explaining all model operations. This reduces computational overhead while maintaining explainability where it is most needed.
3Measurement precision
If neural network models are trained extensively to improve accuracy, then predictive performance improves, but overfitting to training data increases
Solution Approach 1:
The patent incorporates feedback mechanisms where the explanation layer provides insights that can be used to refine the predictive model. This feedback loop helps prevent overfitting by encouraging the model to learn more general patterns that produce consistent and explainable predictions across different datasets.
Solution Approach 2:
The patent changes the training parameters and architecture to balance accuracy and generalization. By adjusting parameters such as regularization strength, dropout rates, and the architecture of the explanation layer, the model achieves good training performance while maintaining reliability on new data.
Data Source
AI summary
A method for generating a classifier, comprising: initializing a neural network with a fully connected architecture; applying a regularization constraint to the set of weights between neurons in the input layer and the plurality of latent features in the hidden layer; iteratively reducing a number of incoming connections to each latent feature in the hidden layer to a predetermined number based on the regularized first set of weights; weighting loss function's value based on categories of activation tuples, wherein contributions of data entries with activation tuples of size greater than a predetermined threshold to the loss function's value are limited and wherein contributions of data entries with activation tuples of size “0” to the loss function's value are minimized, to a predefined percentage; and selectively updating sets of weights associated with top-ranked latent features as evaluated by the magnitude of their contributions at the output layer in a training process.