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Machine learning (ML) pipeline composition and optimisation have been studiedto seek multi-stage ML models, i.e. preprocessor-inclusive, that are both validand well-performing. These processes typically require the design and traversalof complex configuration spaces consisting of not just individual ML componentsand their hyperparameters, but also higher-level pipeline structures that linkthese components together. Optimisation efficiency and resulting ML-modelaccuracy both suffer if this pipeline search space is unwieldy and excessivelylarge; it becomes an appealing notion to avoid costly evaluations of poorlyperforming ML components ahead of time. Accordingly, this paper investigateswhether, based on previous experience, a pool of availableclassifiers/regressors can be preemptively culled ahead of initiating apipeline composition/optimisation process for a new ML problem, i.e. dataset.The previous experience comes in the form of classifier/regressor accuracyrankings derived, with loose assumptions, from a substantial but non-exhaustivenumber of pipeline evaluations; this meta-knowledge is considered'opportunistic'. Numerous experiments with the AutoWeka4MCPS package, includingones leveraging similarities between datasets via the relative landmarkingmethod, show that, despite its seeming unreliability, opportunisticmeta-knowledge can improve ML outcomes. However, results also indicate that theculling of classifiers/regressors should not be too severe either. In effect,it is better to search through a 'top tier' of recommended predictors than topin hopes onto one previously supreme performer.

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