Active Testing Strategy to Predict the Best Classification Algorithm via Sampling and Metalearning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2010

Autores

Rui Leite (Autor) FEP; Pavel Brazdil (Autor) FEP

Ata de Conferência Internacional

Título: ECAI 2010 - 19TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE

Páginas: 309-314

19th European Conference on Artificial Intelligence (ECAI)/6th Conference on Prestigious Applications of Intelligent Systems (PAIS)

Lisbon, PORTUGAL, AUG 16-20, 2010

Indexação

ISI Web of Knowledge - 22 Citações

Scopus - 33 Citações

Outras Informações

ID Authenticus: P-003-AM5

DOI: 10.3233/978-1-60750-606-5-309

Abstract (EN): Currently many classification algorithms exist and there is no algorithm that would outperform all the others in all tasks. Therefore it is of interest to determine which classification algorithm is the best one for a given task. Although direct comparisons can be made for any given problem using a cross-validation evaluation, it is desirable to avoid this, as the computational costs are significant. We describe a method which relies on relatively fast pairwise comparisons involving two algorithms. This method exploits sampling landmarks, that is information about learning curves besides classical data characteristics. One key feature of this method is an iterative procedure for extending the series of experiments used to gather new information in the form of sampling landmarks. Metalearning plays also a vital role. The comparisons between various pairs of algorithm are repeated and the result is represented in the form of a partially ordered ranking. Evaluation is done by comparing the partial order of algorithm that has been predicted to the partial order representing the supposedly correct result. The results of our analysis show that the method has good performance and could be of help in practical applications.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6