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Ensemble classifier and resampling for imbalanced multiclass learning

An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in orde...

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Main Authors: Sainin, Mohd Shamrie, Ahmad, Faudziah, Alfred, Rayner
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/15678/1/PID047.pdf
http://repo.uum.edu.my/15678/
http://www.icoci.cms.net.my/proceedings/2015/TOC.html
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id my.uum.repo.15678
record_format eprints
spelling my.uum.repo.156782016-04-27T08:46:44Z http://repo.uum.edu.my/15678/ Ensemble classifier and resampling for imbalanced multiclass learning Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner QA75 Electronic computers. Computer science An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier 2015 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/15678/1/PID047.pdf Sainin, Mohd Shamrie and Ahmad, Faudziah and Alfred, Rayner (2015) Ensemble classifier and resampling for imbalanced multiclass learning. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html
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 QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sainin, Mohd Shamrie
Ahmad, Faudziah
Alfred, Rayner
Ensemble classifier and resampling for imbalanced multiclass learning
description An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier
format Conference or Workshop Item
author Sainin, Mohd Shamrie
Ahmad, Faudziah
Alfred, Rayner
author_facet Sainin, Mohd Shamrie
Ahmad, Faudziah
Alfred, Rayner
author_sort Sainin, Mohd Shamrie
title Ensemble classifier and resampling for imbalanced multiclass learning
title_short Ensemble classifier and resampling for imbalanced multiclass learning
title_full Ensemble classifier and resampling for imbalanced multiclass learning
title_fullStr Ensemble classifier and resampling for imbalanced multiclass learning
title_full_unstemmed Ensemble classifier and resampling for imbalanced multiclass learning
title_sort ensemble classifier and resampling for imbalanced multiclass learning
publishDate 2015
url http://repo.uum.edu.my/15678/1/PID047.pdf
http://repo.uum.edu.my/15678/
http://www.icoci.cms.net.my/proceedings/2015/TOC.html
_version_ 1644281777111433216
score 13.149126

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