A framework to decompose and develop metafeatures

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Fábio Pinto (Author) FEUP; Carlos Soares (Author) FEUP; João Mendes-Moreira (Author) FEUP

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 32-36

International Workshop on Meta-Learning and Algorithm Selection, MetaSel 2014 - Co-located with 21st European Conference on Artificial Intelligence, ECAI 2014

19 August 2014

Indexing

Scopus - 5 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-00A-4CZ

Abstract (EN): This paper proposes a framework to decompose and develop metafeatures for Metalearning (MtL) problems. Several metafeatures (also known as data characteristics) are proposed in the literature for a wide range of problems. Since MtL applicability is very general but problem dependent, researchers focus on generating specific and yet informative metafeatures for each problem. This process is carried without any sort of conceptual framework. We believe that such framework would open new horizons on the development of metafeatures and also aid the process of understanding the metafeatures already proposed in the state-of-the-art. We propose a framework with the aim of fill that gap and we show its applicability in a scenario of algorithm recommendation for regression problems.

Language: English

Type (Professor's evaluation): Scientific