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Improving ensemble decision tree performance using Adaboost and Bagging

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...

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Main Authors: Hasan, Md Rajib, Siraj, Fadzilah, Sainin, Mohd Shamrie
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
QA76 Computer software
Online Access:http://repo.uum.edu.my/16741/1/14.pdf
http://repo.uum.edu.my/16741/
http://doi.org/10.1063/1.4937027
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id my.uum.repo.16741
record_format eprints
spelling my.uum.repo.167412016-04-27T03:36:28Z http://repo.uum.edu.my/16741/ Improving ensemble decision tree performance using Adaboost and Bagging Hasan, Md Rajib Siraj, Fadzilah Sainin, Mohd Shamrie QA76 Computer software 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. 2015-09-29 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/16741/1/14.pdf 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. http://doi.org/10.1063/1.4937027 doi:10.1063/1.4937027
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Hasan, Md Rajib
Siraj, Fadzilah
Sainin, Mohd Shamrie
Improving ensemble decision tree performance using Adaboost and Bagging
description 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.
format Conference or Workshop Item
author Hasan, Md Rajib
Siraj, Fadzilah
Sainin, Mohd Shamrie
author_facet Hasan, Md Rajib
Siraj, Fadzilah
Sainin, Mohd Shamrie
author_sort Hasan, Md Rajib
title Improving ensemble decision tree performance using Adaboost and Bagging
title_short Improving ensemble decision tree performance using Adaboost and Bagging
title_full Improving ensemble decision tree performance using Adaboost and Bagging
title_fullStr Improving ensemble decision tree performance using Adaboost and Bagging
title_full_unstemmed Improving ensemble decision tree performance using Adaboost and Bagging
title_sort improving ensemble decision tree performance using adaboost and bagging
publishDate 2015
url http://repo.uum.edu.my/16741/1/14.pdf
http://repo.uum.edu.my/16741/
http://doi.org/10.1063/1.4937027
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score 13.211869

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