Machine Learning Perforations for Exception Decision Handling
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Solution Overview
Problem
Existing machine learning models struggle to effectively incorporate exception decisions, leading to potential weakening of model performance due to either overemphasis or underemphasis of exception factors, which can result in inaccurate decision-making.
Innovation Solution
Generate and utilize perforations within machine learning models to identify and store additional features contributing to exception decisions, allowing for separate validation of feature boundaries and deviation scores, thereby enhancing model accuracy in the presence of exceptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models incorporate exception decisions, then the models can learn from anomaly type decisions, but model performance may be weakened due to overemphasis or underemphasis of exception factors
Solution Approach 1:
The patent segments the decision-making process by creating separate perforation models for exception cases distinct from the base model. This allows exception decisions to be handled independently through specific perforation boundaries, preventing them from negatively impacting the overall model performance while still enabling learning from these anomalies.
Solution Approach 2:
The patent introduces perforation models as intermediary structures between the base model and exception decisions. These perforation models act as mediators that capture exception patterns without allowing them to directly affect the base model's core functionality, thus maintaining reliability while improving adaptability.
2Adaptability or versatility
If machine learning models emphasize exception factors, then exception decisions can be better captured, but model accuracy may deteriorate due to overemphasis
Solution Approach 1:
The patent applies local quality by creating perforation models with specific boundaries tailored to exception cases only. These perforation models have localized exception factors that apply specifically to anomaly situations, while the base model maintains its general accuracy for normal cases, thus avoiding overemphasis of exception factors across all decisions.
3Adaptability or versatility
If machine learning models create separate structures for exception analysis, then exception factors can be isolated and analyzed, but device complexity increases
Solution Approach 1:
The patent implements nesting by embedding perforation models within the overall machine learning system architecture. The perforation models are nested components that work in conjunction with the base model, allowing exception analysis functionality to be integrated without creating entirely separate complex systems. This nested structure enables isolated exception analysis while maintaining manageable system complexity.
Data Source
AI summary
An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine a feature associated with the base model in making the exception decision. The embodiment may automatically identify a remaining additional feature in making the exception decision, and generating a perforation corresponding to the remaining additional feature. The embodiment may, in response to detecting a subsequent decision including a shared additional feature to the generated perforation, automatically validate a feature boundary within the generated perforation. The embodiment may automatically outputting a decision recommendation for the subsequent decision using both the base model and the generated perforation.