Singular Ensemble Model for Local ML Execution
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Solution Overview
Problem
Existing ensemble machine learning models require significant processing power and memory resources, making them unsuitable for execution on user devices, and often necessitate combining disparate model types with different output formats, complicating their implementation.
Innovation Solution
The development of singular ensemble machine learning models that combine multiple machine learning models into a single model, allowing for efficient execution on user devices without the need for extensive processing power or memory, and accommodating different model types by mapping and combining their features into a unified structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ensemble machine learning models are used to improve prediction accuracy, then the model accuracy is improved, but the processing power and memory requirements increase significantly
Solution Approach 1:
The patent merges multiple machine learning models into a single unified model architecture that integrates the predictive capabilities of individual models (such as neural networks, linear regression, and decision trees) while consolidating their computational operations. This merging approach maintains the accuracy benefits of ensemble modeling by preserving the collective predictive power of multiple models while executing them as a single computational unit, thereby reducing the processing power and memory requirements compared to running separate ensemble models.
Solution Approach 2:
The singular ensemble model is designed with multi-functionality to handle various types of input models and prediction tasks within a single unified framework. The model can adaptively apply different computational strategies for different input types (e.g., structured data, unstructured data, time-series data) while maintaining a consistent execution path. This universality allows the system to achieve ensemble-level accuracy without requiring multiple specialized model executions, thus reducing overall computational resource consumption.
2Reliability
If traditional ensemble machine learning models are used to improve prediction accuracy, then the model accuracy is improved, but the memory resources required increase significantly
Solution Approach 1:
The patent merges multiple machine learning models into a single unified model architecture that integrates the predictive capabilities of individual models (such as neural networks, linear regression, and decision trees) while consolidating their computational operations. This merging approach maintains the accuracy benefits of ensemble modeling by preserving the collective predictive power of multiple models while executing them as a single computational unit, thereby reducing the processing power and memory requirements compared to running separate ensemble models.
Solution Approach 2:
The singular ensemble model employs segmentation by dividing the unified model into modular functional components that can be selectively activated based on the input type and prediction task. Each segment represents a specific computational function (e.g., feature extraction, pattern recognition, prediction) that can be independently optimized and executed. This segmentation allows the system to load only the necessary computational segments into memory for each prediction task, reducing overall memory requirements while maintaining the accuracy benefits of ensemble modeling.
3Adaptability or versatility
If combinatory functions are used to combine outputs from disparate model types, then the ability to handle different model types is improved, but the complexity of implementation increases
Solution Approach 1:
The singular ensemble model is designed with multi-functionality to handle various types of input models and prediction tasks within a single unified framework. The model can adaptively apply different computational strategies for different input types (e.g., structured data, unstructured data, time-series data) while maintaining a consistent execution path. This universality allows the system to achieve ensemble-level accuracy without requiring multiple specialized model executions, thus reducing overall computational resource consumption.
Solution Approach 2:
Instead of combining the outputs of multiple trained models through complex post-processing combinatory functions, the patent inverts the approach by integrating the computational logic of multiple models directly into a single unified model architecture during the training phase. The singular model learns to perform the functions of multiple models internally and produces a unified prediction output directly, eliminating the need for separate combinatory functions and significantly simplifying the implementation complexity.
Data Source
AI summary
The present disclosure relates to systems and methods for generating and using a singular ensemble model.