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Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2022-03-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in this study is the synthetic minority oversampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) algorithms. The algorithm approach used in this study is the ensemble classifier using AdaBoost and bagging with the classification and regression tree (CART) as learner-based. The best model obtained from the experimental results in accuracy, precision, recall, and f-measure using ADASYN-N and AdaBoost-CART.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data</dc:title>
<dc:creator>Kurniawati, Yulia Ery</dc:creator>
<dc:creator>Prabowo, Yulius Denny</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Adaptive synthetic-nominal class imbalance learning; Ensemble classifier; Over-sampling; Synthetic minority oversampling technique-nominal</dc:subject>
<dc:description xml:lang="en-US">Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in this study is the synthetic minority oversampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) algorithms. The algorithm approach used in this study is the ensemble classifier using AdaBoost and bagging with the classification and regression tree (CART) as learner-based. The best model obtained from the experimental results in accuracy, precision, recall, and f-measure using ADASYN-N and AdaBoost-CART.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2022-03-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21410</dc:identifier>
<dc:identifier>10.11591/ijai.v11.i1.pp276-283</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 276-283</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v11.i1</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21410/13340</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
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Sitasi artikel ini
APA 7
Kurniawati, Y. E., & Prabowo, Y. D. (2022). Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data. IAES International Journal of Artificial Intelligence, 11(1), 276–283. https://doi.org/10.11591/ijai.v11.i1.pp276-283
Salin
IEEE
Y. E. Kurniawati and Y. D. Prabowo, “Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data,” IAES International Journal of Artificial Intelligence, vol. 11, no. 1, pp. 276–283, 2022, doi: 10.11591/ijai.v11.i1.pp276-283.
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