Using Meta-Learning to Support Data Mining

• Tipo: Artigo em Revista Científica Internacional
• Data: 2004

Autores

Ricardo Vilalta (Autor) Outra; Christophe Giraud G Carrier (Autor) Outra; Pavel Brazdil (Autor) FEP; Carlos Soares (Autor) FEP

Revista

Título: IJCSA

Vol. 1 Nº 1

Páginas: 31-45

Outras Informações

ID Authenticus: P-009-33C

Abstract (EN): Current data mining tools are characterized by a plethora of algorithms but a lack of guidelines to select the right method according to the nature of the problem under analysis. Producing such guidelines is a primary goal by the field of meta-learning; the research objective is to understand the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. The field of meta-learning has seen continuous growth in the past years with interesting new developments in the construction of practical model-selection assistants, task-adaptive learners, and a solid conceptual framework. In this paper, we give an overview of different techniques necessary to build meta-learning systems. We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques. We then look at how each technique has been studied and implemented by previous research. In addition, we show how meta- learning has already been identified as an important component in real-world applications.

Idioma: Inglês

Tipo (Avaliação Docente): Científica