# Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA

arXiv: https://arxiv.org/abs/1612.08789 · HTML: https://mlsanity.com/paper/1612.08789/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2016.

## Built on by (most cited first, 8 of 8)

- [Automated Deep Learning: Neural Architecture Search Is Not the End](https://mlsanity.com/paper/2112.09245/) (2021)
- [A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems](https://mlsanity.com/paper/2109.10898/) (2021)
- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning](https://mlsanity.com/paper/2105.00282/) (2021)
- [On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning](https://mlsanity.com/paper/2208.04376/) (2022)
- [Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity](https://mlsanity.com/paper/2402.02124/) (2024)
- [Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search](https://mlsanity.com/paper/2608.18665/) (2026)
- [Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons](https://mlsanity.com/paper/2609.04425/) (2026)

## Abstract (arXiv)

Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.