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Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning

The aim of this paper is to investigate the effects of combining feature selection and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning .This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large...

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Main Authors: Sainin, Mohd Shamrie, Alfred, Rayner, Alias, Suraya, Lammasha, Mohamed A.M.
Format: Conference or Workshop Item
Language:English
Published: 2018
Subjects:
HD28 Management. Industrial Management
Online Access:http://repo.uum.edu.my/25208/1/KMICE%202018%20134%20139.pdf
http://repo.uum.edu.my/25208/
http://www.kmice.cms.net.my/ProcKMICe/KMICe2018/toc.html
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spelling my.uum.repo.252082018-11-25T02:29:54Z http://repo.uum.edu.my/25208/ Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning Sainin, Mohd Shamrie Alfred, Rayner Alias, Suraya Lammasha, Mohamed A.M. HD28 Management. Industrial Management The aim of this paper is to investigate the effects of combining feature selection and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning .This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark data sets in which six ensemble methods from Weka machine learning tool were selected to perform the classification task.These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, and Rotation Forest.In addition, five base classifiers were used; Naïve Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. There are two feature selection approaches implemented which are filter-based (CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval) and wrapper-based (WrapperSubsetEval). The results obtained from the experiments show that although the performance accuracy is not much improved, however, with less number of attributes, the classifiers are able to achieve similar accuracy or slightly improved with less processing time.In knowledge management, the findings provide important insight of which algorithm is suitable for decision making when dealing with high dimensional and large data. 2018-07-25 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/25208/1/KMICE%202018%20134%20139.pdf Sainin, Mohd Shamrie and Alfred, Rayner and Alias, Suraya and Lammasha, Mohamed A.M. (2018) Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning. In: Knowledge Management International Conference (KMICe) 2018, 25 –27 July 2018, Miri Sarawak, Malaysia. http://www.kmice.cms.net.my/ProcKMICe/KMICe2018/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 HD28 Management. Industrial Management
spellingShingle HD28 Management. Industrial Management
Sainin, Mohd Shamrie
Alfred, Rayner
Alias, Suraya
Lammasha, Mohamed A.M.
Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
description The aim of this paper is to investigate the effects of combining feature selection and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning .This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark data sets in which six ensemble methods from Weka machine learning tool were selected to perform the classification task.These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, and Rotation Forest.In addition, five base classifiers were used; Naïve Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. There are two feature selection approaches implemented which are filter-based (CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval) and wrapper-based (WrapperSubsetEval). The results obtained from the experiments show that although the performance accuracy is not much improved, however, with less number of attributes, the classifiers are able to achieve similar accuracy or slightly improved with less processing time.In knowledge management, the findings provide important insight of which algorithm is suitable for decision making when dealing with high dimensional and large data.
format Conference or Workshop Item
author Sainin, Mohd Shamrie
Alfred, Rayner
Alias, Suraya
Lammasha, Mohamed A.M.
author_facet Sainin, Mohd Shamrie
Alfred, Rayner
Alias, Suraya
Lammasha, Mohamed A.M.
author_sort Sainin, Mohd Shamrie
title Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
title_short Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
title_full Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
title_fullStr Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
title_full_unstemmed Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
title_sort feature selection and ensemble meta classifier for multiclass imbalance data learning
publishDate 2018
url http://repo.uum.edu.my/25208/1/KMICE%202018%20134%20139.pdf
http://repo.uum.edu.my/25208/
http://www.kmice.cms.net.my/ProcKMICe/KMICe2018/toc.html
_version_ 1644284258474262528
score 13.211869

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