Exploring Opportunistic Meta-knowledge to Reduce Search Spaces for Automated Machine Learning

TL;DR

This paper explores using prior experience in the form of opportunistic meta-knowledge to preemptively reduce the search space in automated machine learning pipeline optimization, aiming to improve efficiency and model performance.

Contribution

It introduces a method to leverage opportunistic meta-knowledge for culling classifiers/regressors before pipeline search, enhancing AutoML efficiency and effectiveness.

Findings

Opportunistic meta-knowledge can improve ML outcomes.

Moderate culling of classifiers/regressors is beneficial.

Searching within a top tier of predictors outperforms relying on a single best performer.

Abstract

Machine learning (ML) pipeline composition and optimisation have been studied to seek multi-stage ML models, i.e. preprocessor-inclusive, that are both valid and well-performing. These processes typically require the design and traversal of complex configuration spaces consisting of not just individual ML components and their hyperparameters, but also higher-level pipeline structures that link these components together. Optimisation efficiency and resulting ML-model accuracy both suffer if this pipeline search space is unwieldy and excessively large; it becomes an appealing notion to avoid costly evaluations of poorly performing ML components ahead of time. Accordingly, this paper investigates whether, based on previous experience, a pool of available classifiers/regressors can be preemptively culled ahead of initiating a pipeline composition/optimisation process for a new ML problem, i.e. dataset. The previous experience comes in the form of classifier/regressor accuracy rankings derived, with loose assumptions, from a substantial but non-exhaustive number of pipeline evaluations; this meta-knowledge is considered 'opportunistic'. Numerous experiments with the AutoWeka4MCPS package, including ones leveraging similarities between datasets via the relative landmarking method, show that, despite its seeming unreliability, opportunistic meta-knowledge can improve ML outcomes. However, results also indicate that the culling of classifiers/regressors should not be too severe either. In effect, it is better to search through a 'top tier' of recommended predictors than to pin hopes onto one previously supreme performer.