TY - CHAP
T1 - Dynamic Programming Algorithms for Minimization of Decision Tree Complexity
AU - Azad, Mohammad
AU - Chikalov, Igor
AU - Hussain, Shahid
AU - Moshkov, Mikhail
AU - Zielosko, Beata
N1 - KAUST Repository Item: Exported on 2022-12-02
PY - 2022/11/19
Y1 - 2022/11/19
N2 - In this chapter, we present dynamic programming algorithms for minimization of the depth and number of nodes of decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. Decision trees with hypotheses, generally, have less complexity than conventional decision trees, i.e., they are more understandable and more suitable as a means for knowledge representation.
AB - In this chapter, we present dynamic programming algorithms for minimization of the depth and number of nodes of decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. Decision trees with hypotheses, generally, have less complexity than conventional decision trees, i.e., they are more understandable and more suitable as a means for knowledge representation.
UR - http://hdl.handle.net/10754/686117
UR - https://link.springer.com/10.1007/978-3-031-08585-7_3
U2 - 10.1007/978-3-031-08585-7_3
DO - 10.1007/978-3-031-08585-7_3
M3 - Chapter
SN - 9783031085840
SP - 19
EP - 40
BT - Decision Trees with Hypotheses
PB - Springer International Publishing
ER -