Concluding Remarks

Cognitive technologies, pp. 329–337

Abstract

Summary As metaknowledge has a central role in many approaches discussed in this book, we address the issue of what kind of metaknowledge is used in different metalearning/AutoML tasks, such as algorithm selection, hypeparameter optimization, and workflow generation. We draw attention to the fact that some metaknowledge is acquired (learned) by the systems, while other is given (e.g., different aspects of the given configuration space). This chapter continues by discussing future challenges, such as how to achieve better integration of metalearning and AutoML approaches, and what kind of guidance could be provided by the system when configuring metalearning/AutoML systems to new settings. This task may involve (semi-)automatic reduction of configuration spaces to make the search more effective. The last part of this chapter discusses various challenges encountered when trying to automate different steps of data science.

Authors 4

1. Pavel Brazdil corresponding

   Universidade do Porto

   Affiliation as printed

   Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

2. Jan N. van Rijn Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

3. Carlos Soares

   Universidade do Porto

   Affiliation as printed

   Porto Business School, Porto, Portugal

4. Joaquin Vanschoren

   Eindhoven University of Technology

   Affiliation as printed

   Department of Mathematics and Computer Science, Technische Universiteit Eindhoven, Eindhoven, The Netherlands