Cross-Domain Classification Ensembles for Cold-Start Model Training
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Solution Overview
Problem
The cold-start problem in machine learning modeling occurs when a model is not performant until sufficient training data is gathered, particularly when exposed to new types of input data, leading to increased time, processing, and resource expenditure.
Innovation Solution
A computer-implemented method using transfer learning and ensemble models to rapidly deploy performant models in new domains by leveraging knowledge from related domain models through a meta model that aggregates predictions from base models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific machine learning models are trained for various functions, then prediction accuracy is improved, but training time and resource expenditure increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a generalized model on broad domain data before deploying it to specific domains. This pre-trained model serves as a foundation that can be quickly adapted to new domains through transfer learning, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent implements universality by creating a generalized machine learning model that can serve multiple domains and functions. This single model architecture is designed to be adaptable across different domains through transfer learning, replacing the need for multiple separate domain-specific models and reducing overall training resource expenditure.
2Measurement precision
If domain-specific machine learning models are trained for various functions, then prediction accuracy is improved, but processing resources and cost increase
Solution Approach 1:
The patent implements universality by creating a generalized machine learning model that can serve multiple domains and functions. This single model architecture is designed to be adaptable across different domains through transfer learning, replacing the need for multiple separate domain-specific models and reducing overall training resource expenditure.
Solution Approach 2:
The patent applies parameter changes by adjusting model parameters and architecture during transfer learning to adapt the generalized model to specific domains. This allows the model to maintain high prediction accuracy for domain-specific tasks while using a single reusable architecture, reducing the processing resources needed compared to training multiple separate models.
3Productivity
If a generalized model is used to avoid training resources, then resource expenditure is reduced, but prediction accuracy becomes insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training a generalized model on broad domain data before deploying it to specific domains. This pre-trained model serves as a foundation that can be quickly adapted to new domains through transfer learning, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent implements dynamics by making the generalized model adaptable and flexible through transfer learning mechanisms. The model can dynamically adjust its parameters and representations when exposed to new domain data, allowing it to maintain high prediction accuracy across different domains while using a single reusable architecture.
4Reliability
If training data is gathered to overcome the cold-start problem, then model performance is improved, but time and resource expenditure increase
Solution Approach 1:
The patent applies preliminary action by pre-training a generalized model on broad domain data before deploying it to specific domains. This pre-trained model serves as a foundation that can be quickly adapted to new domains through transfer learning, eliminating the need to train from scratch and significantly reducing training time while maintaining accuracy.
Solution Approach 2:
The patent uses transfer learning as an intermediary mechanism that bridges the gap between generalized pre-trained models and domain-specific applications. This intermediary approach allows the model to leverage knowledge from broad domain training while adapting to specific domains with minimal additional data, reducing the time and resources needed to overcome the cold-start problem.
Data Source
AI summary
Certain aspects of the disclosure provide systems and methods for a cross-domain ensemble of machine learning models for onboarding new classification services. A method includes processing a classification task with an ensemble of machine learning models to generate a classification prediction, wherein the ensemble of machine learning comprises a base learner and a meta model, wherein the base learner is trained on a different domain from the classification task. The method further includes training a new classification model based on the classification prediction and data associated with the classification task.