Optimization of β-decision rules relative to number of misclassifications
Type
Conference PaperAuthors
Zielosko, BeataDate
2012Permanent link to this record
http://hdl.handle.net/10754/564505
Metadata
Show full item recordAbstract
In the paper, we present an algorithm for optimization of approximate decision rules relative to the number of misclassifications. The considered algorithm is based on extensions of dynamic programming and constructs a directed acyclic graph Δ β (T). Based on this graph we can describe the whole set of so-called irredundant β-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 Springer-Verlag.Publisher
Springer Science + Business MediaConference/Event name
4th International Conference on Computational Collective Intelligence, ICCCI 2012ISBN
9783642347061ISSN
03029743ae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-34707-8_35