Metalearning

• Type: Chapter or Part of a Book
• Date: 2017

Authors

Pavel Brazdil (Author) FEP; Ricardo Vilalta (Author) Other; Christophe Giraud-Carrier (Author) Other; Carlos Soares (Author) FEUP

Book

Title: Encyclopedia of Machine Learning and Data Mining

Pages: 818-823

ISBN: 978-1-4899-7685-7

Electronic ISBN: 978-1-4899-7685-7

Other information

Authenticus ID: P-00M-NW5

DOI: 10.1007/978-1-4899-7687-1_543

Abstract (EN): In the area machine learning / data mining many diverse algorithms are available nowadays and hence the selection of the most suitable algorithm may be a challenge. Tbhis is aggravated by the fact that many algorithms require that certain parameters be set. If a wrong algorithm and/or parameter configuration is selected, substandard results may be obtained. The topic of metalearning aims to facilitate this task. Metalearning typically proceeds in two phases. First, a given set of algorithms A (e.g. classification algorithms) and datasets D is identified and different pairs < ai,dj > from these two sets are chosen for testing. The dataset di is described by certain meta-features which together with the performance result of algorithm ai constitute a part of the metadata. In the second phase the metadata is used to construct a model, usually again with recourse to machine learning methods. The model represents a generalization of various base-level experiments. The model can then be applied to the new dataset to recommend the most suitable algorithm or a ranking ordered by relative performance. This article provides more details about this area. Besides, it discusses also how the method can be combined with hyperparameter optimization and extended to sequences of operations (workflows).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6