文章基本信息

标题：Using Meta-Learning to Support Data Mining
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作者：Ricardo Vilalta ; Christophe Giraud-Carrier ; Pavel Brazdil 等
期刊名称：International Journal of Computer Science & Applications
印刷版ISSN：0972-9038
出版年度：2004
卷号：II
期号：I
出版社：Technomathematics Research Foundation
摘要：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.