Discovering interesting prediction rules with a genetic algorithm
- 20 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1510, 1322-1329
- https://doi.org/10.1109/cec.1999.782601
Abstract
In essence, the goal of data mining is to discover knowledge which is highly accurate, comprehensible and "interesting" (surprising, novel). Although the literature emphasizes predictive accuracy and comprehensibility, the discovery of interesting knowledge remains a formidable challenge for data mining algorithms. We present a genetic algorithm designed from the scratch to discover interesting rules. Our GA addresses the dependence modelling task, where different rules can predict different goal attributes. This task can be regarded as a generalization of the classification task, where all rules predict the same goal attribute.Keywords
This publication has 3 references indexed in Scilit:
- Elements of Information TheoryPublished by Wiley ,2001
- Discovery of surprising exception rules based on intensity of implicationPublished by Springer Nature ,1998
- On objective measures of rule surprisingnessPublished by Springer Nature ,1998