Hybrid Prediction System Combining Rule-Based and Learning Models
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Solution Overview
Problem
Conventional learning-based prediction models often misinterpret new situations and struggle to generalize due to overfitting, leading to inaccurate predictions in scenarios not present in their historical data, and lack understanding of causality behind patterns.
Innovation Solution
A computer-implemented method that combines predictions from a rule-based model with those from a learning-based model, using a belief update mechanism to integrate causal reasoning and enhance generalization capabilities by weighing predictions based on their performance and adherence to predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a learning-based prediction model is trained on historical data to discover patterns, then the model can make predictions based on learned trends, but the model misinterprets new situations not present in historical data and fails to generalize
Solution Approach 1:
The patent combines rule-based models with learning-based models into a hybrid system. The rule-based component provides causal reasoning and handles novel situations through predefined logic, while the learning-based component captures patterns from historical data. This merging resolves the contradiction by ensuring both generalization capability (through rules) and prediction accuracy (through learned patterns) are maintained simultaneously.
Solution Approach 2:
The prediction system is constructed as a composite of two different modeling approaches: rule-based reasoning and machine learning. Each component contributes its strengths - rules provide interpretability and handling of unseen scenarios, while the learning model provides pattern recognition from data. This composite structure enables the system to generalize to new situations while maintaining reliable predictions.
2Loss of information
If a learning-based model learns patterns from historical data, then the model can identify trends, but the model overfits to training data and cannot understand causality behind patterns
Solution Approach 1:
The system segments the prediction task into two distinct components: pattern recognition (handled by the learning-based model) and causal reasoning (handled by the rule-based model). This segmentation allows each component to specialize - the learning model extracts patterns from data without needing to understand causality, while the rule-based model provides causal interpretation, together achieving both pattern recognition and causal understanding.
3Productivity
If conventional learning-based models are used for prediction, then the models can process large datasets efficiently, but the models produce inaccurate predictions for scenarios outside training data distribution
Solution Approach 1:
The rule-based model acts as an intermediary that guides and constrains the learning-based model's predictions. When the learning model processes large datasets efficiently, the rule-based component validates predictions against causal logic and predefined constraints, correcting inaccuracies for out-of-distribution scenarios. This intermediary role maintains both data processing efficiency and prediction accuracy.
Data Source
AI summary
One embodiment of a method for processing data includes performing one or more operations to determine a performance of one or more predefined rules based on data that is received and one or more first predictions generated using the one or more predefined rules, performing one or more operations to determine a performance of a trained machine learning model based on the data and one or more second predictions generated using the trained machine learning model, processing the data using the one or more predefined rules to generate one or more third predictions, processing the data using the trained machine learning model to generate one or more fourth predictions, and generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model.