Building algorithm profiles for prior model selection in knowledge discovery systems

被引：0

作者：

Hilario, Melanie ^{[1
]}

Kalousis, Alexandras ^{[1
]}

机构：

[1] CSD, University of Geneva, CH-1211 Geneva 4, Switzerland

来源：

International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications | 2000年 / 8卷 / 02期

关键词：

Knowledge acquisition - Knowledge based systems - Learning algorithms - Vectors;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We propose the use of learning algorithm profiles to address the model selection problem in knowledge discovery systems. These profiles consist of metalevel feature value vectors which describe learning algorithms from the point of view of their representation and functionality, efficiency, resilience and practicality. Values for these features are assigned on the basis of author specifications, expert consensus or previous empirical studies. We review past evaluations of the better known learning algorithms and suggest an experimental strategy for building algorithm profiles on more quantitative grounds. Preliminary experiments have disproved expert judgments on certain algorithm features, thus showing the need to build and refine such profiles via controlled experiments.

引用

页码：77 / 87