Representation of knowledge by decision trees for decision tables with multiple decisions
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Permanent link to this recordhttp://hdl.handle.net/10754/665815
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AbstractIn this paper, we study decisions trees for decision tables with multiple decisions as a means for knowledge representation. To this end, we consider three methods to design decision trees and evaluate the number of nodes, and local and global misclassification rates of constructed trees. The considered methods are based on a dynamic programming algorithm for bi-objective optimization of decision trees. The goal of this study is to construct trees with reasonable number of nodes and at the same time reasonable accuracy. Previously, it was mentioned that the consideration of only the global misclassification rate of the decision tree is not enough and it is necessary to study also the local misclassification rate. The reason is that even if the global misclassification rate related to the whole tree is enough small, the local misclassification rate related to the terminal nodes of the tree can be too big. One of the considered methods allows us to construct the decision trees with moderate number of nodes as well as moderate global and local misclassification rates. These decision trees can be used for the knowledge representation.
CitationAzad, M., Chikalov, I., & Moshkov, M. (2020). Representation of Knowledge by Decision Trees for Decision Tables with Multiple Decisions. Procedia Computer Science, 176, 653–659. doi:10.1016/j.procs.2020.09.037
SponsorsResearch reported in this publication was supported by Jouf University and by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to the anonymous reviewers for useful comments.
Conference/Event name24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020