Algorithm selection via meta-learning and sample-based active testing

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Abdulrahman, SM (Autor) Outra; Pavel Brazdil (Autor) FEP; Van Rijn, JN (Autor) Outra; Vanschoren, J (Autor) Outra

Ata de Conferência Internacional

Título: CEUR Workshop Proceedings

Páginas: 55-66

International Workshop on Meta-Learning and Algorithm Selection, MetaSel 2015 - co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015, ECMLPKDD 2015

7 September 2015

Indexação

Scopus - 9 Citações

Outras Informações

ID Authenticus: P-00G-QJ3

Abstract (EN): Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.

Idioma: Inglês

Tipo (Avaliação Docente): Científica