Abstract
Providing user support for the application of Data Mining algorithms in the field of Knowledge Discovery in Databases (KDD) is an important issue. Based on ideas from the fields of statistics, machine learning and knowledge engineering we provided a general framework for defining user support. The general framework contains a combined top-down and bottom-up strategy to tackle this problem. In the current paper we describe the Algorithm Selection Tool (AST) that is one component in our framework. AST is designed to support algorithm selection in the knowledge discovery process with a case-based reasoning approach. We discuss the architecture of AST and explain the basic components. We present the evaluation of our approach in a systematic analysis of the case retrieval behaviour and thus of the selection support offered by our system.
currently glindner@wuerttag.de
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Lindner, G., Studer, R. (2000). Algorithm Selection Support for Classification. In: Decker, R., Gaul, W. (eds) Classification and Information Processing at the Turn of the Millennium. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57280-7_18
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DOI: https://doi.org/10.1007/978-3-642-57280-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67589-1
Online ISBN: 978-3-642-57280-7
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