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

TL;DR

This paper extends Auto-WEKA to automatically compose and optimize multicomponent predictive systems, significantly increasing the search space and demonstrating the benefits of automated data transformation pipelines across diverse datasets.

Contribution

The paper introduces an extension of Auto-WEKA to support multicomponent predictive systems with a Petri net-based search space, enabling automated composition of complex data preprocessing and modeling pipelines.

Findings

Extended search space from 22,000 to 812 billion combinations

Automated MCPS composition improves predictive performance

Diverse MCPSs are beneficial for different datasets

Abstract

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.