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Improving ensemble decision tree performance using Adaboost and Bagging

Hasan, Md Rajib and Siraj, Fadzilah and Sainin, Mohd Shamrie (2015) Improving ensemble decision tree performance using Adaboost and Bagging. In: 2nd Innovation and Analytics Conference & Exhibition (IACE 2015), 29 September –1 October 2015, TH Hotel, Alor Setar, Kedah, Malaysia.

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Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers.However, in a ensemble settings the performance depends on the selection of suitable base classifier.This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted.The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 978-0-7354-1338-2
Uncontrolled Keywords: Decision trees, Biomedical modeling Data sets
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Multimedia Technology & Communication
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 03 Jan 2016 09:03
Last Modified: 27 Apr 2016 03:36
URI: http://repo.uum.edu.my/id/eprint/16741

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