Genetic Programming for Stacked ML Ensemble Pipeline Selection
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Solution Overview
Problem
Developers face challenges in selecting suitable stacked machine learning model ensemble pipeline architectures, as current methods require trial-and-error and are time-consuming, often resulting in unsuitable architectures for processing specific data sets.
Innovation Solution
A method using genetic programming to generate and score stacked machine learning model ensemble pipeline architectures, iteratively selecting and mutating architectures based on performance scores to converge on an optimized configuration, including the number of layers, models, and hyperparameters, thereby automating the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error methods are used to select stacked machine learning model ensemble pipeline architectures, then developers can find suitable architectures, but the process is time-consuming and often results in unsuitable architectures
Solution Approach 1:
The system performs self-service by automatically generating, scoring, and selecting pipeline architectures without human intervention. The genetic programming algorithm autonomously evolves architectures that meet performance criteria, eliminating the need for manual trial-and-error exploration while ensuring reliable architecture selection.
Solution Approach 2:
The system performs preliminary action by pre-generating multiple pipeline architecture candidates using genetic programming before final selection. This preliminary generation and scoring process creates a pool of evaluated architectures, allowing the system to select the best option without time-consuming manual testing during deployment.
2Extent of automation
If genetic programming is used to generate and score multiple pipeline architectures iteratively, then the selection process is automated and optimized, but the computational complexity increases
Solution Approach 1:
The system segments the architecture selection process into distinct automated stages: generation of candidate architectures, scoring of each candidate, selection of top performers, and mutation for the next generation. This segmentation allows complex automation to be managed through modular, independent operations that can be executed systematically.
Solution Approach 2:
The system manages computational complexity by controlling parameter changes in the genetic programming process, such as limiting the number of generations, adjusting mutation rates, and setting scoring thresholds. These parameter adjustments balance automation extent with computational resource consumption.
3Measurement precision
If the system generates and evaluates multiple generations of architectures, then optimization accuracy improves, but the processing time increases
Solution Approach 1:
The system uses feedback from scoring results to guide the evolution of subsequent generations. By evaluating architectures against performance metrics and using these scores to inform selection and mutation operations, the system achieves accurate optimization without requiring excessive generations, as each generation benefits from learned insights about successful architecture patterns.
Solution Approach 2:
The system applies partial action by generating and evaluating only the necessary number of generations required to achieve satisfactory optimization accuracy. Rather than exhaustively exploring all possible architectures, the system performs sufficient iterations to converge on high-quality solutions, avoiding unnecessary processing time while maintaining optimization precision.
Data Source
AI summary
The exemplary embodiments may provide a stacked machine learning model ensemble pipeline architecture selector that selects a well-suited stacked machine learning model ensemble pipeline architecture for a specified configuration input and a target data set. The stacked machine learning model ensemble pipeline architecture selector may generate and score possible stacked machine learning model ensemble pipeline architectures to locate one that is well-suited for the target data set and the conforms with the configuration input. The stacked machine learning model ensemble pipeline architecture selector may use genetic programming to generate successive generations of possible stacked ensemble pipeline architectures and to score those architectures to determine how well-suited they are. In this manner, the stacked machine learning model ensemble pipeline architecture selector may converge on an architecture that is well-suited, for example, that meet one or more scores, evaluation metrics, and/or the like.