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Optimization of Approximate Decision Rules Relative to Number of Misclassifications: Comparison of Greedy and Dynamic Programming Approaches., , , and . KES (Selected Papers), volume 7828 of Lecture Notes in Computer Science, page 41-50. Springer, (2012)Bounds on Average Weighted Depth of Decision Trees., and . Fundam. Informaticae, 31 (2): 145-156 (1997)Classifiers Based on Optimal Decision Rules., , , and . Fundam. Informaticae, 127 (1-4): 151-160 (2013)On Average Time Complexity of Decision Trees and Branching Programs.. Fundam. Informaticae, 39 (4): 337-357 (1999)Greedy Algorithms for Construction of Approximate Tests for Decision Tables with Many-Valued Decisions., , , and . Fundam. Informaticae, 120 (3-4): 231-242 (2012)Totally optimal decision trees for Boolean functions., , and . Discret. Appl. Math., (2016)Diagnosis of constant faults in iteration-free circuits over monotone basis., , , and . Discret. Appl. Math., (2014)Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories., , , and . J. Intell. Learn. Syst. Appl., 3 (3): 155-170 (2011)Sorting by Decision Trees with Hypotheses (extended abstract)., , , and . CS&P, volume 2951 of CEUR Workshop Proceedings, page 126-130. CEUR-WS.org, (2021)Average Depth and Number of Misclassifications for Decision Trees., , and . CS&P, volume 928 of CEUR Workshop Proceedings, page 160-169. CEUR-WS.org, (2012)