An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features

Multi-class imbalance shape-based leaf image features requires feature subset that appropriately represent the leaf shape.Multi-class imbalance data is a type of data classification problem in which some data classes is highly underrepresented compared to others.This occurs when at least one data cl...

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Main Authors: Sainin, Mohd Shamrie, Alfred, Rayner, Ahmad, Faudziah, Lammasha, Mohamed A.M
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語言:English
出版: Universiti Teknikal Malaysia Melaka 2017
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spelling my.uum.repo.217352017-04-19T08:39:42Z http://repo.uum.edu.my/21735/ An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features Sainin, Mohd Shamrie Alfred, Rayner Ahmad, Faudziah Lammasha, Mohamed A.M QA75 Electronic computers. Computer science Multi-class imbalance shape-based leaf image features requires feature subset that appropriately represent the leaf shape.Multi-class imbalance data is a type of data classification problem in which some data classes is highly underrepresented compared to others.This occurs when at least one data class is represented by just a few numbers of training samples known as the minority class compared to other classes that make up the majority class.To address this issue in shapebased leaf image feature extraction, this paper discusses the evaluation of several methods available in Weka and a wrapperbased genetic algorithm feature selection. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed application/pdf en cc_by http://repo.uum.edu.my/21735/1/JTECE%20%209%20%201-2%202017%2057%2061.pdf Sainin, Mohd Shamrie and Alfred, Rayner and Ahmad, Faudziah and Lammasha, Mohamed A.M (2017) An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features. Journal of Telecommunication, Electronic and Computer Engineering, 9 (1-2). pp. 57-61. ISSN 2180-1843 http://journal.utem.edu.my/index.php/jtec/article/view/1656
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
Alfred, Rayner
Ahmad, Faudziah
Lammasha, Mohamed A.M
An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
description Multi-class imbalance shape-based leaf image features requires feature subset that appropriately represent the leaf shape.Multi-class imbalance data is a type of data classification problem in which some data classes is highly underrepresented compared to others.This occurs when at least one data class is represented by just a few numbers of training samples known as the minority class compared to other classes that make up the majority class.To address this issue in shapebased leaf image feature extraction, this paper discusses the evaluation of several methods available in Weka and a wrapperbased genetic algorithm feature selection.
format Article
author Sainin, Mohd Shamrie
Alfred, Rayner
Ahmad, Faudziah
Lammasha, Mohamed A.M
author_facet Sainin, Mohd Shamrie
Alfred, Rayner
Ahmad, Faudziah
Lammasha, Mohamed A.M
author_sort Sainin, Mohd Shamrie
title An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
title_short An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
title_full An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
title_fullStr An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
title_full_unstemmed An evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
title_sort evaluation of feature selection methods on multi-class imbalance and high dimensionality shape-based leaf image features
publisher Universiti Teknikal Malaysia Melaka
publishDate 2017
url http://repo.uum.edu.my/21735/1/JTECE%20%209%20%201-2%202017%2057%2061.pdf
http://repo.uum.edu.my/21735/
http://journal.utem.edu.my/index.php/jtec/article/view/1656
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