Sampling-based relative landmarks: Systematically test-driving algorithms before choosing

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2001

Autores

Soares, C (Autor) FEP; Petrak, J (Autor) Outra; Brazdil, P (Autor) FEP

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 88-95

10th Portuguese Conference on Artificial Intelligence, EPIA 2001

Porto, 17 December 2001 through 20 December 2001

Indexação

ISI Web of Knowledge

Scopus - 26 Citações

Outras Informações

ID Authenticus: P-008-6VC

DOI: 10.1007/3-540-45329-6_12

Abstract (EN): When facing the need to select the most appropriate algorithm to apply on a new data set, data analysts often follow an approach which can be related to test-driving cars to decide which one to buy: apply the algorithms on a sample of the data to quickly obtain rough estimates of their performance. These estimates are used to select one or a few of those algorithms to be tried out on the full data set. We describe sampling-based landmarks (SL), a systematization of this approach, building on earlier work on landmarking and sampling. SL are estimates of the performance of algorithms on a small sample of the data that are used as predictors of the performance of those algorithms on the full set. We also describe relative landmarks (RL), that address the inability of earlier landmarks to assess relative performance of algorithms. RL aggregate landmarks to obtain predictors of relative performance. Our experiments indicate that the combination of these two improvements, which we call Sampling-based Relative Landmarks, are better for ranking than traditional data characterization measures. © Springer-Verlag Berlin Heidelberg 2001.

Idioma: Inglês

Tipo (Avaliação Docente): Científica