Abstract:
Data Mining aims to discover novel, interesting, and useful
knowledge and patterns from databases. Classification is a data mining technique
which addresses the problem of constructing a predication model for a class
attribute given the values of other attributes and some examples of records with
known class. Decision tree are one of the most well-established classification
methods. They are so popular because their ability to handle nisy data, their
comprehensibility, and their capability to learn disjunctive expression. One of the
most popular decision tree construction algorithm is C4.5. The idea of esemble
methodology is to build a predictive model by integrating multiple models for
better generalization error. It is well known that ensemble method can be used for
improving the predictive performance. Boosting is one of the methods for build
ensemble of classifier. This paper compare s the popular C4.5 and boosted C4.5
for their prediction accuracy using holdout method.
