Multi-objective Machine Learning Optimization Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning optimization approaches face challenges in multi-objective optimization, particularly in providing a variety of solutions and efficiently optimizing hyperparameters and model parameters simultaneously, leading to increased model training and inference time, and requiring deep prior knowledge of the problem and model.
Innovation Solution
Combining hyperparameter optimization (HPO) and model parameter optimization (MPO) techniques to generate a fused Pareto frontier, allowing for the selection of optimal hyperparameters and model parameters based on multiple objectives, reducing processing time while maintaining quality, and applicable to various machine learning models and metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current machine learning optimization approaches are used for multi-objective optimization, then a single objective can be optimized, but multiple objectives cannot be optimized simultaneously and efficiently
Solution Approach 1:
The patent combines hyperparameter optimization (HPO) and model parameter optimization (MPO) into a unified multi-objective optimization framework. This merging allows simultaneous optimization of multiple objectives including hyperparameters, model parameters, and performance metrics, resolving the contradiction between multi-objective capability and optimization efficiency
Solution Approach 2:
The optimization problem is segmented into distinct components: hyperparameter optimization, model parameter optimization, and performance metric optimization. Each component is optimized separately but integrated through the Pareto frontier framework, enabling efficient multi-objective optimization while maintaining clarity in the optimization process
2Ease of manufacture
If hyperparameter optimization and model parameter optimization are performed separately, then each can be optimized independently, but the total processing time increases
Solution Approach 1:
The patent merges HPO and MPO into a simultaneous optimization process where both hyperparameters and model parameters are optimized together through a unified objective function. This integration maintains the independence of each optimization component while eliminating the sequential processing time penalty
Solution Approach 2:
The framework performs preliminary screening of hyperparameter configurations using HPO, then uses these pre-optimized hyperparameters to guide MPO. This preliminary action reduces the search space for subsequent optimization, significantly reducing total processing time while maintaining independence of the optimization processes
3Manufacturing precision
If comprehensive optimization of multiple parameters is performed, then optimization quality improves, but computational complexity increases
Solution Approach 1:
The comprehensive optimization problem is segmented into manageable sub-problems: HPO for hyperparameter selection, MPO for model parameter optimization, and performance metric optimization. This segmentation maintains high optimization quality by addressing each aspect thoroughly while reducing computational complexity through modular processing
Solution Approach 2:
The framework performs partial optimization by focusing computational resources on the most critical parameters at each stage. HPO first identifies promising hyperparameter configurations, then MPO refines model parameters for those specific configurations. This partial action approach achieves high optimization quality without the excessive computational complexity of optimizing all parameters simultaneously
Data Source
AI summary
Techniques for utilizing model and hyperparameter optimization for multi-objective machine learning are disclosed. In one example, a method comprises the following steps. One of a plurality of hyperparameter optimization operations and a plurality of model parameter optimization operations are performed to generate a first solution set. The other of the plurality of hyperparameter optimization operations and the plurality of model parameter optimization operations are performed to generate a second solution set. At least a portion of the first solution set and at least a portion of the second solution set are combined to generate a third solution set.