KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Permanent link to this recordhttp://hdl.handle.net/10754/672059
MetadataShow full item record
AbstractIn this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses about values of all attributes. This approach is similar to one studied in exact learning, where membership and equivalence queries are considered. We propose dynamic programming algorithms for the minimization of the number of nodes in such decision trees and discuss results of computer experiments.
CitationAzad, M., Chikalov, I., Hussain, S., & Moshkov, M. (2021). Minimizing Number of Nodes in Decision Trees with Hypotheses. Procedia Computer Science, 192, 232–240. doi:10.1016/j.procs.2021.08.024
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to anonymous reviewers for useful comments and suggestions.
JournalProcedia Computer Science
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.