TY - JOUR
T1 - Extensions of dynamic programming as a new tool for decision tree optimization
AU - Alkhalid, Abdulaziz
AU - Chikalov, Igor
AU - Hussain, Shahid
AU - Moshkov, Mikhail
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2013
Y1 - 2013
N2 - The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.
AB - The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.
UR - http://hdl.handle.net/10754/562512
UR - http://link.springer.com/10.1007/978-3-642-28699-5_2
UR - http://www.scopus.com/inward/record.url?scp=84879294429&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28699-5_2
DO - 10.1007/978-3-642-28699-5_2
M3 - Article
SN - 2190-3018
VL - 13
SP - 11
EP - 29
JO - Smart Innovation, Systems and Technologies
JF - Smart Innovation, Systems and Technologies
ER -