Feature Space Analysis for Machine Learning Model Training
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Solution Overview
Problem
Conventional machine learning models often suffer from poor separability of feature space regions, leading to suboptimal performance, including high false positives and false negatives, especially when training data is inadequate, and lack confidence metrics for specific inputs.
Innovation Solution
The solution involves analyzing feature space data during training to identify and address issues of separability and performance by adjusting model parameters, structure, or loss functions, using metrics like Bhattacharyya distance and clustering algorithms to improve model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are trained with standard approaches, then training can be completed with basic computational resources, but the model achieves poor separability of feature space regions leading to suboptimal performance
Solution Approach 1:
The patent applies preliminary action by performing feature space analysis during the training process to identify regions of poor separability before the model is deployed. This allows the training process to proactively address separability issues by generating additional training data or adjusting parameters in advance, rather than discovering performance problems only after deployment. The analysis of feature space distributions and identification of overlapping regions occurs during training, enabling preventive corrective actions.
Solution Approach 2:
The patent implements feedback by using the results of feature space analysis to dynamically adjust the training process. The system continuously monitors separability metrics (such as Bhattacharyya distance) during training and uses this information to guide subsequent training steps, including generating additional synthetic training data in regions of poor separability or adjusting data augmentation parameters. This closed-loop feedback mechanism ensures that training efforts are directed toward improving specific problematic regions in the feature space.
2Reliability
If training data is increased to improve model performance, then model accuracy can be enhanced, but computational resources and training time are consumed
Solution Approach 1:
The patent applies local quality by focusing training efforts on specific local regions of the feature space where separability is poor, rather than uniformly processing all training data. The system identifies specific clusters or regions with overlapping class distributions and generates additional synthetic training data targeted at these local problem areas. This localized approach improves model accuracy in critical regions without the computational cost of uniformly increasing training data across the entire feature space.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting data augmentation parameters and synthetic data generation parameters based on feature space analysis results. When regions of poor separability are detected, the system modifies parameters such as the number of synthetic samples to generate, the type of data augmentation applied, or the sampling strategy for existing data. These parameter adjustments are made adaptively during training based on measured separability metrics, optimizing the balance between training time and model accuracy.
3Reliability
If feature space analysis is performed during training, then model accuracy and confidence metrics are improved, but computational overhead is increased
Solution Approach 1:
The patent applies partial action by performing feature space analysis on a subset of training data or on sampled points rather than analyzing every single training example. The system may select representative samples from different classes and regions of the feature space to compute separability metrics, rather than exhaustively analyzing the entire training set. This partial analysis provides sufficient information to guide training adjustments while significantly reducing the computational overhead compared to complete feature space analysis.
Data Source
AI summary
Systems and methods are provided for using results of feature space analysis during the training of machine learning models to improve the training process and the resulting trained model.