ANN Model Optimization via Entity Extraction and Iterative Analysis
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Solution Overview
Problem
Current techniques for optimizing artificial neural network (ANN) classification models and training data fail to provide a clear understanding of the quality of training data, leading to frequent retraining and potential model failures when encountering new data variations.
Innovation Solution
A method and system that extract entities and domain-specific entities from training data, determine model parameters, identify missing data, and iteratively analyze the relative advantage of modified ANN models and training data to optimize model behavior and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques are used to process training data, then model accuracy may be improved, but the ability to understand the impact of training data on model behavior is limited
Solution Approach 1:
The system implements feedback mechanisms by analyzing model predictions and comparing them against expected outcomes. This feedback loop enables the system to understand which training data variations impact model behavior, thereby maintaining high accuracy while gaining interpretability. The feedback is used to iteratively refine the understanding of data-model relationships.
Solution Approach 2:
The patent introduces intermediary components such as data analysis modules and model interpretation layers that mediate between the training data and the model. These intermediaries process and analyze the relationships between training data characteristics and model outcomes, enabling accurate measurement of model accuracy while preserving understanding of the underlying data impacts.
2Ease of manufacture
If training data is used without understanding variations and sufficiency, then initial model training is simplified, but frequent retraining is required to maintain accuracy
Solution Approach 1:
The system performs preliminary analysis of training data variations and sufficiency before model training. By pre-assessing the quality, coverage, and representativeness of training data, the system ensures that the initial model training is both easy to implement and produces a model that maintains accuracy without frequent retraining. This preliminary action prevents future retraining needs.
Solution Approach 2:
The patent employs parameter changes by adjusting training data selection criteria, data augmentation parameters, and model hyperparameters based on the analyzed variations and sufficiency metrics. These parameter adjustments optimize the initial training process while ensuring the model generalizes well to new variations, reducing the need for frequent retraining.
3Device complexity
If training data lacks coverage of variations, then data processing is simpler, but the ANN model fails when encountering newer variations
Solution Approach 1:
The system implements dynamic data processing that adapts to the diversity of variations in the training data. Rather than using static, simple processing pipelines, the system dynamically adjusts processing strategies based on the detected variations, ensuring comprehensive coverage while maintaining manageable complexity. This dynamic approach enhances model robustness to new variations.
Solution Approach 2:
The patent addresses variation coverage by adding another dimension to the training data through data augmentation techniques. By generating synthetic variations and augmenting existing data across multiple dimensions (e.g., transformations, augmentations, synthetic samples), the system achieves comprehensive variation coverage without significantly increasing processing complexity, thereby improving model reliability.
4Productivity
If insufficient training data is used, then training efficiency is improved, but models develop inherited biases and poor accuracy
Solution Approach 1:
The system uses copying and duplication strategies to efficiently expand the effective training data. By creating copies of existing data with various transformations and augmentations, the system achieves high model accuracy without requiring proportionally more raw data, thus maintaining training efficiency while eliminating biases through diverse data representations.
Data Source
AI summary
This disclosure relates to method and system for optimizing artificial neural network (ANN) classification model and training data thereof for appropriate model behavior. The method may include extracting entities and domain specific entities from the training data for each of classes of the ANN classification mode, determining model parameters of the ANN classification model based on the training data, determining missing data with respect to the training data or the model parameters based on the entities and the domain specific entities for each the classes, iteratively analysing a relative advantage of a modified ANN classification model with a modified training data with respect to the ANN classification model with the training data, and determining an optimized ANN classification model and an optimized training data for appropriate model behavior based on the iterative analysis. The modified data may be generated based on the missing data.