Methods › General › AutoML › AutoParsimony
Automatic Search for Parsimonious Models
AutoParsimony
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The principle of parsimony, also known as Occam's razor, elucidates the preference for the simplest explanation that provides optimal results when faced with multiple options. Thus, we can assert that the principle of parsimony is justified by "the assumption that is both the simplest and contains all the necessary information required to comprehend the experiment at hand." This principle finds application in various scenarios or events in our daily lives, including predictions in Data Science models.
It is widely recognized that a less complex model will produce more stable predictions, exhibit greater resilience to noise and disturbances, and be more manageable for maintenance and analysis. Additionally, reducing the number of features can lead to further cost savings by diminishing the use of sensors, lowering energy consumption, minimizing information acquisition costs, reducing maintenance requirements, and mitigating the necessity to retrain models due to feature fluctuations caused by noise, outliers, data drift, etc.
The concurrent optimization of hyperparameters (HO) and feature selection (FS) for achieving Parsimonious Model Selection (PMS) is an ongoing area of active research. Nonetheless, the effective selection of appropriate hyperparameters and feature subsets presents a challenging combinatorial problem, frequently requiring the application of efficient heuristic methods.
Papers archive 2025-07-28
13 shown of 13, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets 1 Dec 2023 · 1 repository
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PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections 1 Sep 2023 · 1 repository
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Artificial Intelligence Models for Assessing the Evaluation Process of Complex Student Projects 20 Feb 2023 · 0 repositories
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An advanced methodology to enhance energy efficiency in a hospital cooling-water system 1 Nov 2021 · 0 repositories
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A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package 10 Sep 2021 · 0 repositories
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Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency 17 Feb 2021 · 0 repositories
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Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection 18 Aug 2019 · 0 repositories
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Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components 1 Sep 2018 · 0 repositories
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Evaluation of a novel GA-based methodology for model structure selection: The GA-PARSIMONY 3 Jan 2018 · 1 repository
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Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning 6 Sep 2017 · 0 repositories
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Searching parsimonious solutions with GA-PARSIMONY and XGboost in high-dimensional databases 1 Oct 2016 · 0 repositories
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GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace 1 Oct 2015 · 1 repository
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A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel 1 Jan 2015 · 0 repositories
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| feature selection | 10 |
| Model Selection | 5 |
| Hyperparameter Optimization | 4 |
| Bayesian Optimization | 1 |
| Demand Forecasting | 1 |
| Diversity | 1 |
| Feature Engineering | 1 |
| Scheduling | 1 |
| regression | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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