KAUST DepartmentComputer Science Program
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2019-05-27
Print Publication Date2019
Embargo End Date2020-05-27
Permanent link to this recordhttp://hdl.handle.net/10754/656505
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AbstractIn this paper, we experimentally study the existence of totally optimal decision rules which are optimal relative to the length and coverage simultaneously for nine decision tables from the UCI Machine Learning Repository. Totally optimal rules can be useful when we consider decision rules as a way for knowledge representation. We study not only exact but also approximate decision rules based on the three uncertainty measures: entropy, Gini index, and misclassification error. To investigate the existence of totally optimal rules, we use an extension of dynamic programming that allows us to make multi-stage optimization of decision rules relative to the length and coverage. Experimental results show that totally optimal decision rules exist in many cases. However, the behavior of graphs describing how the number of rows of decision tables with totally optimal decision rules depends on the accuracy of rules is irregular.
CitationAzad, M., & Moshkov, M. (2019). Experimental Study of Totally Optimal Decision Rules. Lecture Notes in Computer Science, 319–326. doi:10.1007/978-3-030-20915-5_29
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to the anonymous reviewer for useful comments.