# Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines

arXiv: https://arxiv.org/abs/2312.14770 · HTML: https://mlsanity.com/paper/2312.14770/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [LightAutoML: AutoML Solution for a Large Financial Services Ecosystem](https://mlsanity.com/paper/2109.01528/) (2021)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties](https://mlsanity.com/paper/2009.12710/) (2020)
- [Amazon SageMaker Autopilot: a white box AutoML solution at scale](https://mlsanity.com/paper/2012.08483/) (2020)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines](https://mlsanity.com/paper/2106.15397/) (2021)
- [Layered TPOT: Speeding up Tree-based Pipeline Optimization](https://mlsanity.com/paper/1801.06007/) (2018)
- [Incremental Search Space Construction for Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2101.10951/) (2021)

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

- [SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling](https://mlsanity.com/paper/2609.12455/) (2026)

## Abstract (arXiv)

Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task independent constructs: their general composition remains the same, regardless of the data. In contrast, the structure of flexible pipelines varies depending on the input, making them finely tailored to individual tasks. However, flexible pipelines can be structurally overcomplicated and have poor explainability. We propose the EVOSA approach that compensates for the negative points of flexible pipelines by incorporating a sensitivity analysis which increases the robustness and interpretability of the flexible solutions. EVOSA quantitatively estimates positive and negative impact of an edge or a node on a pipeline graph, and feeds this information to the evolutionary AutoML optimizer. The correctness and efficiency of EVOSA was validated in tabular, multimodal and computer vision tasks, suggesting generalizability of the proposed approach across domains.