Evolutionary Boosting Machine for Simultaneous Feature and Hyperparameter Optimization
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Solution Overview
Problem
Traditional machine learning methodologies face inefficiencies in simultaneously optimizing feature selection and hyperparameter tuning, leading to sub-optimal models, especially in high-dimensional datasets, where the complexity of solution spaces makes exhaustive search computationally infeasible.
Innovation Solution
The implementation of an evolutionary boosting machine that uses cycles to automatically determine optimal hyperparameters and feature sets through genetic algorithm generations, allowing for simultaneous optimization of feature selections and hyperparameters, ensuring the 'black box' in wrapper feature selection is optimal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional machine learning methodologies are used to select features and tune hyperparameters in independent stages, then the process is simpler to implement, but the model optimization is sub-optimal and computational efficiency is reduced
Solution Approach 1:
The patent merges feature selection and hyperparameter tuning into a single unified optimization process using evolutionary algorithms. Instead of performing these tasks in separate independent stages, the system simultaneously optimizes both features and hyperparameters together, allowing the search space to explore combinations that yield superior model performance while maintaining computational efficiency through parallel evaluation of candidate solutions.
2Measurement precision
If exhaustive search is performed to determine optimal feature sets in high-dimensional datasets, then complete optimization is achieved, but computational feasibility is compromised due to NP-hard complexity
Solution Approach 1:
The patent replaces exhaustive mechanical search with evolutionary algorithms that use biological inspiration principles (selection, crossover, mutation) to navigate the search space. This substitution allows the system to achieve high-quality optimizations without requiring complete enumeration of all possible feature combinations, making the previously NP-hard problem computationally feasible by using stochastic search heuristics instead of systematic exhaustion.
Solution Approach 2:
The patent changes the approach from fixed exhaustive search to dynamic evolutionary search where hyperparameters of the optimization process itself (population size, mutation rate, crossover probability) are adjusted based on performance. This allows the system to adaptively allocate computational resources to promising regions of the search space, achieving effective optimization without the rigid computational burden of exhaustive methods.
3Loss of time
If feature selection is performed before hyperparameter tuning, then the feature set is determined early, but the optimality of the feature set is lost whenhyperparameters are changed
Solution Approach 1:
The patent introduces dynamics to the optimization process by allowing both feature sets andhyperparameter values to evolve simultaneously across generations. Rather than fixing features early and then tuning hyperparameters statically, the system dynamically adjusts both components based on feedback from model performance evaluations, ensuring that the final solution represents the optimal combination rather than a compromised intermediate result.
Data Source
AI summary
Aspects relate to a machine learning system implementing an evolutionary boosting machine. The system may initially select randomized feature sets for an initial generation of candidate models. Evolutionary algorithms may be applied to the system to create later generations of the cycle, combining and mutating the feature selections of the candidate models. The system may determine optimal number of boosting iterations for each candidate model in a generation by building boosting iterations from an initial value up to a predetermined maximum number of boosting iterations. When a final generation is achieved, the system may evaluate the optimal model of the generation. If the optimal boosting iterations of the optimal model does not meet solution constraints on the optimal boosting iterations, the system may adjust a learning rate parameter and then proceed to the next cycle. Based on termination criteria, the system may determine a resulting/final optimal mode.