Beschreibung
A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. Work on building decision trees for datasets exists in multiple disciplines such as signal processing, pattern recognition, decision theory, statistics, machine learning and artificial neural networks. This thesis deals with the problem of finding the parameter settings of decision tree algorithm in order to build accurate, small trees, and to reduce execution time for a given domain.
Autorenporträt
Name: Radhwan Hussein allSagheer. Degree: Assistant Lecturer since 2016. Master(2016): Computer Engineering - Osmania University - Hyderabad - India, Appreciation: very good, Graduation Project: Improving The Efficiency Of C4.5 Algorithm For Data Mining Prediction.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
