Synthetic Data Generator for AI Outlier Detection
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Solution Overview
Problem
Artificial intelligence models suffer from hindsight bias and overfitting, particularly when detecting outlier events, due to their reliance on historical data patterns, leading to poor performance on new and unpredictable scenarios.
Innovation Solution
The system generates synthetic data to train models, applying model constraints to prevent overfitting and enhance detection of unpredictable events by using a synthetic data generator model with constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are trained on historical data to learn patterns, then model performance on routine scenarios is improved, but model ability to detect outlier events deteriorates
Solution Approach 1:
The system performs preliminary action by generating synthetic outlier data before actual outlier events occur. The synthetic data generator creates realistic representations of rare events based on historical patterns, allowing the model to be pre-trained on these artificial outlier scenarios. This preliminary exposure enables the model to recognize and detect actual outlier events when they occur in production, resolving the contradiction between learning from historical patterns and detecting unpredictable events.
2Measurement precision
If models learn from training data including noise and fluctuations, then model performance on historical data is improved, but model generalization to new data deteriorates
Solution Approach 1:
The system extracts only the essential patterns and structures from historical data while deliberately excluding noise and random fluctuations. The synthetic data generator learns the underlying data distribution and generates clean, structured representations of outlier events without copying the noise present in original historical data. This extraction process allows the model to learn genuine patterns that generalize to new data while avoiding memorization of historical noise.
Solution Approach 2:
The system changes parameters by transforming historical data into synthetic representations with modified characteristics. The synthetic data generator adjusts data parameters to create realistic outlier scenarios while controlling the level of noise and variability. This parameter transformation enables the model to learn robust patterns that are insensitive to specific noise realizations in the training data, improving generalization performance.
3Productivity
If models are trained to predict routine events, then model efficiency on common scenarios is improved, but model responsiveness to unpredictable events deteriorates
Solution Approach 1:
The system merges routine historical data with synthetic outlier data into a unified training dataset. The synthetic data generator creates augmented training data that combines common scenarios with rare events, allowing the model to be trained on both routine and outlier events simultaneously. This merging enables the model to maintain high efficiency on common predictions while also developing the capability to detect unpredictable events, resolving the contradiction between productivity and adaptability.
Data Source
AI summary
Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and/or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.