Decision Rules Derived from Optimal Decision Trees with Hypotheses
Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2021-12-07Permanent link to this record
http://hdl.handle.net/10754/673940
Metadata
Show full item recordAbstract
Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees.Citation
Azad, M., Chikalov, I., Hussain, S., Moshkov, M., & Zielosko, B. (2021). Decision Rules Derived from Optimal Decision Trees with Hypotheses. Entropy, 23(12), 1641. doi:10.3390/e23121641Sponsors
Research funded by King Abdullah University of Science and Technology.Publisher
MDPI AGJournal
EntropyAdditional Links
https://www.mdpi.com/1099-4300/23/12/1641ae974a485f413a2113503eed53cd6c53
10.3390/e23121641