Skip to main content

Decision Trees for Knowledge Representation

Author name : MOHAMMAD MOHIUDDIN AZAD
Publication Date : 2019-12-01
Journal Name : CEUR Workshop Proceedings

Abstract

In this paper, we consider decision trees as a means of knowledge representation. To this end, we design three algorithms for decision tree construction that are based on extensions of dynamic programming. We study three parameters of the decision trees constructed by these algorithms: number of nodes, global misclassification rate, and local misclassification rate.

Keywords

Decision Trees

Publication Link

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiK8pLJmvH6AhV9gf0HHe-KAnAQFnoECAcQAQ&url=http%3A%2F%2Fceur-ws.org%2FVol-2571%2FCSP2019_paper_1.pdf&usg=AOvVaw0uUANOUab7_pk5-cbwnemm

Block_researches_list_suggestions

Suggestions to read

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things
Ahlem . Harchy Ep Berguiga
Generalized first approximation Matsumoto metric
AMR SOLIMAN MAHMOUD HASSAN
Structure–Performance Relationship of Novel Azo-Salicylaldehyde Disperse Dyes: Dyeing Optimization and Theoretical Insights
EBTSAM KHALEFAH H ALENEZY
“Synthesis and Characterization of SnO₂/α-Fe₂O₃, In₂O₃/α-Fe₂O₃, and ZnO/α-Fe₂O₃ Thin Films: Photocatalytic and Antibacterial Applications”
Asma Arfaoui
Contact