Latent Feature Classifier Training for Imbalanced Data Interpretability
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Solution Overview
Problem
Neural network models often suffer from over-specified architectures, leading to reduced robustness and predictive power, and struggle with class imbalance, making it difficult to accurately identify minority class instances, while lacking interpretability and transparency.
Innovation Solution
A method for training neural networks using class coverage-based interpretable latent features, which iteratively selects and partitions latent features based on coverage efficiency metrics to enhance model robustness and interpretability, addressing class imbalance by focusing on single or paired input variables and constraining slack features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use fully connected dense architecture with many latent features, then predictive power increases, but model robustness decreases and degrees of freedom increase
Solution Approach 1:
The patent segments the dense network architecture into sparse subnetworks by selectively pruning connections based on importance scoring. This divides the monolithic dense structure into functional modules that maintain predictive power while reducing overall complexity and degrees of freedom, thereby improving robustness.
Solution Approach 2:
The patent extracts and removes unnecessary connections and latent features from the dense neural network through iterative pruning. By taking out redundant elements while preserving critical ones (identified through importance scoring), the model achieves better robustness without sacrificing essential predictive capabilities.
2Measurement precision
If neural networks are trained to detect minority class instances, then detection rate improves, but false positive rate among majority class increases
Solution Approach 1:
The patent applies local quality by training specialized sparse subnetworks that focus on specific aspects of the data relevant to minority class detection. Each pruned subnetwork develops local expertise in detecting particular patterns, improving minority class detection while maintaining controlled false positive rates through distributed specialization.
Solution Approach 2:
The patent introduces importance scoring as an intermediary mechanism that mediates between minority class detection and false positive control. The scoring system evaluates connection relevance and guides pruning decisions, acting as a mediator to balance detection sensitivity with false positive suppression during the model training and refinement process.
3Measurement precision
If neural networks use complex latent features with many layers, then predictive relationships improve, but interpretability and transparency decrease
Solution Approach 1:
The patent segments the complex deep network into simpler sparse subnetworks with fewer layers and connections. This segmentation makes the model architecture more interpretable while preserving predictive relationships through the distributed arrangement of specialized subnetworks, allowing stakeholders to understand individual subnetwork functions.
Solution Approach 2:
The patent changes architectural parameters by enforcing sparsity constraints and limiting network density. This parameter change simplifies the model structure, reducing the number of latent features and layers while maintaining predictive power through efficient connection selection, thereby improving interpretability and transparency.
4Productivity
If standard training approaches are used on imbalanced datasets, then training efficiency improves, but performance on minority class deteriorates
Solution Approach 1:
The patent applies preliminary action by performing importance scoring and connection pruning before final model training. This pre-processing step prepares the network architecture to be more receptive to minority class patterns by removing distractions and focusing computational resources on relevant features, improving minority class performance without sacrificing training efficiency.
Solution Approach 2:
The patent introduces dynamic adaptability through iterative pruning and retraining cycles. The model dynamically adjusts its architecture by removing poorly performing connections and reinforcing important ones, allowing it to adapt to the imbalanced dataset characteristics and improve minority class detection while maintaining overall training efficiency.
Data Source
AI summary
A method for generating a classifier, wherein the method comprises generating a set of candidate latent features from a training dataset, evaluating, by the at least one processor, each of the candidate latent features based on a coverage efficiency metric, wherein the coverage efficiency metric balances detection of minority class instances against a minimization of false positives among majority class instances; selecting, by the at least one processor, a first latent feature from the set of candidate latent features based on a ranking of the coverage efficiency metric associated with each of the candidate latent features; partitioning the training dataset by marking the training records identified by the first latent feature as covered records and removing the covered records from the training dataset to form an uncovered dataset; identifying slack features from remaining candidate latent features, training a neural network model using the selected latent features and the slack features.