Metalearning in Ensemble Methods

Cognitive technologies, pp. 189–200

Abstract

Abstract This chapter discusses some approaches that exploit metalearning methods in ensemble learning. It starts by presenting a set of issues, such as the ensemble method used, which affect the process of ensemble learning and the resulting ensemble. In this chapter we discuss various lines of research that were followed. Some approaches seek an ensemble-based solution for the whole dataset, others for individual instances. Regarding the first group, we focus on metalearning in the construction, pruning and integration phase. Modeling the interdependence of models plays an important part in this process. In the second group, the dynamic selection of models is carried out for each instance. A separate section is dedicated to hierarchical ensembles and some methods used in their design. As this area involves potentially very large configuration spaces, recourse to advanced methods, including metalearning, is advantageous. It can be exploited to define the competence regions of different models and the dependencies between them.

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