Preprocessor Selection for Machine Learning Pipelines

By Brandon Schoenfeld, Christophe G. Giraud-Carrier, Mason Poggemann, J. Christensen, Kevin Seppi. 2018

13 citations recorded by the source corpus.

Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing. Yet, it is generally well accepted that machine learning applications require not only model building, but also data preprocessing. In other words, practical solutions consist of pipelines of machine learning operators rather than single algorithms. Interestingly, our experiments suggest that, on average, data preprocessing hinders accuracy, while the best performing pipelines do actually make use of preprocessors. Here, we conduct an extensive empirical study over a wide range of learning algorithms and preprocessors, and use metalearning to determine when one should make use of preprocessors in ML pipeline design.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marc-André Zöller
2. Marco F. Huber
3. Li Yang
4. Abdallah Shami

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Using information on which preprocessing and model combination performs well, potentially better performing pipelines can be favored (Post et al., 2016; Bilalli et al., 2017; Schoenfeld et al., 2018).”
• Survey on Automated Machine Learning (2019, background): “Using information which preprocessor and model combination perform well together, potentially better performing pipelines can obtain a higher ranking (Schoenfeld et al., 2018).”