# An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

arXiv: https://arxiv.org/abs/2504.06207 · HTML: https://mlsanity.com/paper/2504.06207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 104 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Gradient-based Hyperparameter Optimization through Reversible Learning](https://mlsanity.com/paper/1502.03492/) (2015)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Multi-fidelity Gaussian Process Bandit Optimisation](https://mlsanity.com/paper/1603.06288/) (2016)
- [OBOE: Collaborative Filtering for AutoML Model Selection](https://mlsanity.com/paper/1808.03233/) (2018)
- [A Comprehensive Overview and Survey of Recent Advances in Meta-Learning](https://mlsanity.com/paper/2004.11149/) (2020)
- [Investigating the Duality of Interpretability and Explainability in Machine Learning](https://mlsanity.com/paper/2503.21356/) (2025)
- [On Parameter Tuning in Meta-learning for Computer Vision](https://mlsanity.com/paper/2003.00837/) (2020)
- [Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters](https://mlsanity.com/paper/2008.00025/) (2020)
- [A Feature Subset Selection Algorithm Automatic Recommendation Method](https://mlsanity.com/paper/1402.0570/) (2014)
- [autoBagging: Learning to Rank Bagging Workflows with Metalearning](https://mlsanity.com/paper/1706.09367/) (2017)
- [Learning Abstract Task Representations](https://mlsanity.com/paper/2101.07852/) (2021)
- [Model Lake: a New Alternative for Machine Learning Models Management and Governance](https://mlsanity.com/paper/2503.22754/) (2025)

## Built on by (most cited first, 1 of 1)

- [Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture](https://mlsanity.com/paper/2508.08005/) (2025)

## Abstract (arXiv)

Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparameters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.