Multi-Objective Hyperparameter Optimization (MOHPO)
Overview
Multi-Objective Hyperparameter Optimization (MOHPO) is a sophisticated approach designed to optimize machine learning (ML) pipelines across multiple objectives, extending beyond mere predictive accuracy. This method addresses the complexities of hyperparameter optimization (HPO) in machine learning, particularly when multiple evaluation criteria are involved. MOHPO is essential for improving the expected generalization performance of ML models, providing a framework for comparing solution sets, and enhancing the explainability and trustworthiness of optimization processes.
Architecture
MOHPO employs evolutionary algorithms (EAs) inspired by natural evolution, which are particularly suited for multi-objective optimization. The architecture involves initializing a population of solutions, evaluating them, and iteratively selecting better solutions to generate offspring. Key methodologies include:
- Multi-Objective Evolutionary Algorithms (MOEAs): Such as MOEA/D and SMS-EMOA, which optimize multiple scalar problems simultaneously.
- Multi-Fidelity Methods: These allocate additional resources to configurations that perform well with fewer resources.
- Bayesian Optimization: Utilizes probabilistic surrogates of the objective function to enhance resource allocation and model training.
Goals
The primary goals of MOHPO include:
- Minimizing estimated generalization error and various performance metrics (e.g., classification accuracy, energy consumption, and prediction error).
- Optimizing multiple objectives simultaneously, such as model size and prediction time.
- Addressing bias in ML models to ensure fairness across sub-groups.
- Enhancing the robustness of models against adversarial attacks and perturbations.
Dataset Information
MOHPO supports various dataset types, including:
- CIFAR-10
- MNIST
- CIFAR-100
- ImageNet
- Boston Housing
- COMPAS (Correctional Offender Management Profiling for Alternative Sanctions)
Data is often split into training, validation, and test sets to facilitate effective evaluation.
Outputs
The outputs of MOHPO include:
- A suite of Pareto optimal trade-offs for model selection.
- Quality indicators that measure the quantitative difference between solution sets.
- Performance metrics such as classification error, precision, recall, and prediction time.
Techniques and Modules
Several techniques are integral to MOHPO:
- Multi-Objective Optimization (MOO): Optimizes multiple objectives simultaneously and approximates efficient solutions.
- Nested Resampling Techniques: Avoids optimistic bias in model performance assessment.
- Quality Indicators: Facilitate the comparison of algorithms in multi-objective optimization.
- Normalization of Objectives: Prevents bias towards certain objectives by transforming the objective space.
Relationship to Other Methods
MOHPO builds on existing techniques and methodologies, including:
- Evolutionary Algorithms: Such as NSGA-II and SPEA2, which are tailored for multi-objective optimization.
- Bayesian Optimization: Enhances the efficiency of hyperparameter tuning.
- Random Search: Demonstrated to outperform grid search in multi-objective scenarios.
Limitations and Open Questions
Despite its advancements, MOHPO faces several challenges:
- The absence of commonly accepted quality indicators for measuring solution set quality in MOO.
- Limited software implementations and benchmarks specifically for MOHPO.
- The need for further research on integrating user preferences and handling unsupervised methods.
Evaluation
Evaluation settings for MOHPO involve:
- A three-way split of the data into training, validation, and test sets.
- Multi-objective evaluation metrics, including hypervolume indicators and inverted generational distance.
- Base models such as DenseNet-121 and Random Forest are used for performance comparisons.
Conclusion
Multi-Objective Hyperparameter Optimization represents a significant advancement in optimizing machine learning models across multiple criteria. By addressing the complexities of hyperparameter tuning and providing a robust framework for evaluating trade-offs, MOHPO enhances the performance and reliability of machine learning applications.