2024-03-28T08:48:38Z
https://meral.edu.mm/oai
oai:meral.edu.mm:recid/3631
2021-12-13T00:45:53Z
1582963302567:1597824273898
user-ucsy
Comparative Study of C4.5 and Boosting using Decision Tree Learning Algorithm
Tun, Thinzar
Win, Chit Nilar
Data Mining aims to discover novel, interesting, and usefulknowledge and patterns from databases. Classification is a data mining techniquewhich addresses the problem of constructing a predication model for a classattribute given the values of other attributes and some examples of records withknown class. Decision tree are one of the most well-established classificationmethods. They are so popular because their ability to handle nisy data, theircomprehensibility, and their capability to learn disjunctive expression. One of themost popular decision tree construction algorithm is C4.5. The idea of esemblemethodology is to build a predictive model by integrating multiple models forbetter generalization error. It is well known that ensemble method can be used forimproving the predictive performance. Boosting is one of the methods for buildensemble of classifier. This paper compare s the popular C4.5 and boosted C4.5for their prediction accuracy using holdout method.
2011-12-29
http://hdl.handle.net/20.500.12678/0000003631
https://meral.edu.mm/records/3631