KAUST DepartmentOffice of the VP
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/666478
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AbstractIn 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.
SponsorsResearch 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 name28th International Workshop on Concurrency, Specification and Programming, CS and P 2019
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).