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.
|Original language||English (US)|
|Title of host publication||28th International Workshop on Concurrency, Specification and Programming, CS and P 2019|
|State||Published - Jan 1 2019|
Bibliographical noteKAUST 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.