Algorithm Recommendation for Data Streams

Cognitive technologies, pp. 201–218

Abstract

Abstract This chapter focuses on metalearning approaches that have been applied to data streams. This is an important area, as many real-world data arrive in the form of a stream of observations. We first review some important aspects of the data stream setting, which may involve online learning, non-stationarity, and concept drift.

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