TY - GEN
T1 - Decision trees for knowledge representation
AU - Azad, Mohammad
AU - Chikalov, Igor
AU - Moshkov, Mikhail
N1 - KAUST Repository Item: Exported on 2020-12-19
Acknowledgements: 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.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/666478
UR - http://ceur-ws.org/Vol-2571/CSP2019_paper_1.pdf
UR - http://www.scopus.com/inward/record.url?scp=85082111697&partnerID=8YFLogxK
M3 - Conference contribution
BT - 28th International Workshop on Concurrency, Specification and Programming, CS and P 2019
PB - [email protected]
ER -