Using Meta-Learning to Support Data Mining

توضیحات

ABSTRACT

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 metalearning has already been identified as an important component in real-world applications.

INTRODUCTION

Meta-learning differs from base-learning in the scope of the level of adaptation; whereas learning at the base-level is focused on accumulating experience on a specific learning task (e.g., credit rating, medical diagnosis, mine-rock discrimination, fraud detection, etc.), learning at the meta-level is concerned with accumulating experience on the performance of multiple applications of a learning system. If a base-learner fails to perform efficiently, one would expect the learning mechanism itself to adapt in case the same task is presented again. Briefly stated, the field of meta-learning is focused on the relation between tasks or domains and learning strategies. In that sense, by learning or explaining what causes a learning system to be successful or not on a particular task or domain, we go beyond the goal of producing more accurate learners to the additional goal of understanding the conditions (e.g., types of example distributions) under which a learning strategy is most appropriate.

Year:2004

Publisher:LIACC

By: Ricardo Vilalta , Christophe Giraud-Carrier , Pavel Brazdil , Carlos Soares

File Information:English Language/ 15Page / size:191KB

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سال:2004

ناشر :LIACC

کاری از : Ricardo Vilalta , Christophe Giraud-Carrier , Pavel Brazdil , Carlos Soares

اطلاعات فایل : زبان انگلیسی / 15صفحه / حجم : 191KB

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