Abstract
The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
Author supplied keywords
Cite
CITATION STYLE
Alkhalid, A., Chikalov, I., & Moshkov, M. (2010). On algorithm for building of optimal α-decision trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6086 LNAI, pp. 438–445). https://doi.org/10.1007/978-3-642-13529-3_47
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
