Optimization and analysis of decision trees and rules: Dynamic programming approach
Amin, Talha M.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/564783
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AbstractThis paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.
PublisherInforma UK Limited