Type
Conference PaperKAUST Department
Office of the VPComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Academic Affairs
Applied Mathematics and Computational Science Program
Date
2019-01-01Permanent link to this record
http://hdl.handle.net/10754/666478
Metadata
Show full item recordAbstract
In this paper, we consider decision trees as a means of knowledge representation. To this end, we design three algorithms for decision tree construction that are based on extensions of dynamic programming. We study three parameters of the decision trees constructed by these algorithms: number of nodes, global misclassification rate, and local misclassification rate.Sponsors
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to the anonymous reviewers for useful comments.Conference/Event name
28th International Workshop on Concurrency, Specification and Programming, CS and P 2019Additional Links
http://ceur-ws.org/Vol-2571/CSP2019_paper_1.pdf
Except where otherwise noted, this item's license is described as Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).