Multi-Interval Model Ensembles for Adaptive Prediction Selection
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Solution Overview
Problem
Existing machine learning models fail to account for changing environments and realities by not considering ongoing data, leading to inefficiencies and inaccurate predictions due to over-fitting and lack of nuance in model selection.
Innovation Solution
A method that generates model ensembles by scoring and selecting models based on incomplete test data, using a combination of error and non-error metrics to improve model performance and adapt to changing circumstances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems simply utilize the most-accurate model at each interval, then prediction accuracy at individual stages is improved, but over-fitting occurs and model selection lacks nuance
Solution Approach 1:
The patent combines multiple prediction models into an ensemble that collectively evaluates data across all intervals rather than selecting a single model per interval. This merging approach prevents over-fitting by distributing the prediction task across multiple models while maintaining nuanced evaluation of changing environments throughout the project lifecycle.
Solution Approach 2:
The patent creates a universal model ensemble that can evaluate data across multiple intervals and project stages simultaneously. This multi-functional ensemble replaces the need for separate interval-specific models, providing both accuracy and robustness through its ability to adapt to changing environments while maintaining consistent evaluation criteria across all stages.
2Stability of the object's composition
If models are trained using prior data without consideration of ongoing data, then training stability is improved, but models fail to account for changing environments and realities
Solution Approach 1:
The patent implements feedback by continuously evaluating ongoing project data through the model ensemble and using this evaluation to assess model performance. The system incorporates feedback from incomplete test data at each interval, allowing models to adapt to changing environments while maintaining training stability through structured evaluation and selection criteria.
Solution Approach 2:
The patent creates a dynamic system where the model ensemble continuously adapts to changing project environments by evaluating ongoing data. Rather than static models trained once on historical data, the ensemble dynamically adjusts its evaluation based on current project status, interval progress, and emerging patterns in the data.
3Device complexity
If existing approaches fail to account for ongoing data evolution, then model simplicity is maintained, but prediction accuracy deteriorates in changing environments
Solution Approach 1:
The patent segments the prediction task into multiple intervals and creates an ensemble of models, each specialized for evaluating data at specific project stages. This segmentation allows the system to maintain relative simplicity within each interval while achieving high overall accuracy through the coordinated evaluation of multiple specialized models across the complete project lifecycle.
Data Source
AI summary
Techniques for model evaluation and selection are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received, and a plurality of model ensembles, each specifying one or more of the plurality of models for each of the plurality of intervals, is generated. A test data set is received, where the test data set includes values for at least a first interval of the plurality of intervals and does not include values for at least a second interval of the plurality of intervals. A first model ensemble, of the plurality of model ensembles, is selected based on processing the test data set using each of the plurality of model ensembles.