# Incremental Search Space Construction for Machine Learning Pipeline Synthesis

arXiv: https://arxiv.org/abs/2101.10951 · HTML: https://mlsanity.com/paper/2101.10951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [High Dimensional Bayesian Optimisation and Bandits via Additive Models](https://mlsanity.com/paper/1503.01673/) (2015)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar](https://mlsanity.com/paper/1905.10345/) (2019)
- [AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model](https://mlsanity.com/paper/2001.11158/) (2020)

## Built on by (most cited first, 6 of 6)

- [Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines](https://mlsanity.com/paper/2106.15397/) (2021)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [Benchmarking Automated Machine Learning Methods for Price Forecasting Applications](https://mlsanity.com/paper/2304.14735/) (2023)
- [Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms](https://mlsanity.com/paper/2310.04238/) (2023)
- [Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines](https://mlsanity.com/paper/2312.14770/) (2023)
- [AutoQML: A Framework for Automated Quantum Machine Learning](https://mlsanity.com/paper/2502.21025/) (2025)

## Abstract (arXiv)

Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. Many studies have investigated efficient methods for algorithm selection and hyperparameter optimization. However, methods for ML pipeline synthesis and optimization considering the impact of complex pipeline structures containing multiple preprocessing and classification algorithms have not been studied thoroughly. In this paper, we propose a data-centric approach based on meta-features for pipeline construction and hyperparameter optimization inspired by human behavior. By expanding the pipeline search space incrementally in combination with meta-features of intermediate data sets, we are able to prune the pipeline structure search space efficiently. Consequently, flexible and data set specific ML pipelines can be constructed. We prove the effectiveness and competitiveness of our approach on 28 data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.