Multi-Stage Rare Event Prediction for Imbalanced AI Training
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Solution Overview
Problem
Existing prediction models struggle with accurately predicting rare events due to imbalanced training datasets, where a significant portion of the model capacity is wasted identifying easy negative patterns, making it difficult to learn complex patterns of hard negatives and positives.
Innovation Solution
A multi-stage rare event prediction system is introduced, comprising a primary prediction model trained on an imbalanced dataset, followed by secondary prediction models trained on data points with the highest primary prediction scores, focusing on learning complex patterns of hard negatives and positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single prediction model is trained on an imbalanced dataset to predict rare events, then the model can be trained efficiently, but the prediction accuracy for rare events deteriorates due to wasted model capacity on easy negative patterns
Solution Approach 1:
The patent divides the prediction task into multiple stages with different models. The first model handles the imbalanced dataset efficiently, while subsequent models focus specifically on hard negative and positive examples. This segmentation allows each model to specialize in particular aspects of the prediction problem, improving overall accuracy without sacrificing training efficiency.
Solution Approach 2:
The patent introduces a new dimension to the training process by creating synthetic hard negative examples through data augmentation and by adding prediction scores as additional features. This transforms the original 2D problem (features vs. label) into a higher-dimensional space that captures the complexity of rare event patterns more effectively.
2Speed
If model capacity is allocated to identify easy negative patterns in imbalanced datasets, then training converges faster, but the ability to learn complex patterns of hard negatives and positives deteriorates
Solution Approach 1:
The first prediction model performs preliminary classification of examples, identifying which samples are hard negatives or hard positives. This preliminary action allows subsequent models to focus their capacity on these difficult cases rather than wasting it on easy negatives, thereby improving pattern recognition capability while maintaining training efficiency.
Solution Approach 2:
Different parts of the model ensemble are assigned different qualities or specializations. The first model is optimized for overall classification efficiency, while subsequent models are specifically tailored to handle hard examples. This local quality differentiation ensures that each model contributes its strengths to the overall prediction system.
Data Source
AI summary
Systems and methods for performing rare event prediction using artificial intelligence. The method includes training a primary prediction model, such as XGBoost, using a first training dataset; using the trained primary prediction model to generate a primary prediction score for a rare event for each data point in a second training dataset; generating a modified second training dataset by selecting k data points of the second training dataset with a highest primary prediction score to form the modified second training dataset and adding the corresponding primary prediction score to each of the k data points of the modified second training dataset as a feature, wherein k is an integer greater than one; training a secondary prediction model using the modified second training dataset; and forming a multi-stage rare event prediction system from the trained primary prediction model and the trained secondary prediction model.