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  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 . © 2010 Springer-Verlag Berlin Heidelberg.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)