From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision Tables
Identifieur interne : 000142 ( LNCS/Analysis ); précédent : 000141; suivant : 000143From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision Tables
Auteurs : Christophe Mues [Royaume-Uni, Belgique] ; Bart Baesens [Royaume-Uni] ; Rudy Setiono [Singapour] ; Jan Vanthienen [Belgique]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: The advent of knowledge discovery in data (KDD) technology has created new opportunities to analyze huge amounts of data. However, in order for this knowledge to be deployed, it first needs to be validated by the end-users and then implemented and integrated into the existing business and decision support environment. In this paper, we propose a framework for the development of business intelligence (BI) systems which centers on the use of neural network rule extraction and decision tables. Two different types of neural network rule extraction algorithms, viz. Neurolinear and Neurorule, are compared, and subsequent implementation strategies based on decision tables are discussed.
Url:
DOI: 10.1007/11590019_55
Affiliations:
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