Bi-criteria Optimization Problem for Decision and Inhibitory Trees: Cost Versus Cost
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Applied Mathematics and Computational Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/653082
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AbstractIn this chapter, we study bi-criteria optimization problem cost versus cost for decision and inhibitory trees. We design an algorithm which constructs the set of Pareto optimal points for bi-criteria optimization problem for decision trees, and show how the constructed set can be transformed into the graphs of functions that describe the relationships between the studied cost functions. We extend the obtained results to the case of inhibitory trees. We consider two applications: study of 12 greedy heuristics as algorithms for single- and bi-criteria optimization of decision and inhibitory trees, and study of two relationships for decision trees related to knowledge representation—number of nodes versus depth and number of nodes versus average depth.
CitationAlsolami F, Azad M, Chikalov I, Moshkov M (2019) Bi-criteria Optimization Problem for Decision and Inhibitory Trees: Cost Versus Cost. Intelligent Systems Reference Library: 121–139. Available: http://dx.doi.org/10.1007/978-3-030-12854-8_8.