Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

TL;DR

This paper presents an automated evolutionary framework for designing composite machine learning pipelines, integrating workflow management, hyperparameter tuning, and sensitivity analysis to improve model performance across various tasks.

Contribution

It introduces a novel evolutionary approach for pipeline design that combines workflow management, hyperparameter tuning, and sensitivity analysis, with an open-source software implementation.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effective in classification, regression, and time series tasks

Validated through extensive experiments

Abstract

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to computation workflows that consist of models and data operations. The approach combines key ideas of both automated machine learning and workflow management systems. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The evolutionary approach is used for the flexible identification of pipeline structure. The additional algorithms for sensitivity analysis, atomization, and hyperparameter tuning are implemented to improve the effectiveness of the approach. Also, the software implementation on this approach is presented as an open-source framework. The set of experiments is conducted for the different datasets and tasks (classification, regression, time series forecasting). The obtained results confirm the correctness and effectiveness of the proposed approach in the comparison with the state-of-the-art competitors and baseline solutions.