Black-Box Model Optimization Through Feature Weighting
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Solution Overview
Problem
Machine learning models are considered 'black boxes,' making it difficult to optimize them effectively due to a lack of understanding of how they produce output results based on input data.
Innovation Solution
A method and apparatus for model optimization that involves determining service models, selecting a target model, identifying candidate feature data, selecting target feature data, inputting data into the model to obtain results, determining weights using an optimization algorithm to minimize differences, and optimizing the model based on these weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to execute services, then service execution capability is improved, but model optimization difficulty increases due to black box nature
Solution Approach 1:
The patent introduces an intermediary optimization system that sits between the black box model and the user. This system includes: (1) an optimization module that automatically adjusts model parameters based on performance metrics, (2) a feature analysis component that identifies important input features, and (3) a parameter tuning mechanism that modifies hyperparameters. These intermediaries translate the opaque model operations into actionable optimization insights without requiring users to understand the internal black box mechanisms.
Solution Approach 2:
The patent implements self-service optimization capabilities where the model system automatically performs its own optimization. This includes: (1) automated performance monitoring that tracks model metrics, (2) self-diagnosis mechanisms that identify performance bottlenecks, and (3) automatic retraining pipelines that improve the model based on new data. The system serves itself by continuously optimizing without external intervention, reducing the complexity burden on users.
2Measurement precision
If feature data is selected and weighted to optimize model output, then model accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the feature data processing into distinct manageable components: (1) feature selection module that identifies relevant features from the input dataset, (2) feature weighting module that assigns importance scores to selected features, and (3) feature transformation module that prepares features for model input. This segmentation allows each component to be optimized independently, improving accuracy while controlling computational complexity through modular processing.
Solution Approach 2:
The patent applies local quality by focusing computational resources on the most important features rather than processing all features uniformly. The feature weighting mechanism identifies and emphasizes critical features with high impact on model output while reducing or eliminating less important features. This localized focus on high-value features improves model accuracy efficiently without the computational cost of processing all features at full detail.
Data Source
AI summary
This specification discloses model optimization methods and apparatuses, devices, and storage media. A model with a low service result accuracy rate can be selected from service models as a target model, and therefore fitting can be performed based on input feature data of the target model and an output result of the target model. Therefore, a weight value corresponding to each feature dimension of the feature data input into the target model can be determined. Further, data of specific feature dimensions of the feature data that are more concerned by the target model can be determined based on the determined weight value corresponding to each feature dimension of the feature data, and the target model is optimized based on feature dimensions concerned by the target model.