The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction
A conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2021-05-01
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| Series: | Jurnal Informatika |
| Subjects: | |
| Online Access: | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9969 |
| _version_ | 1818609083584872448 |
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| author | Binti Solihah Azhari Azhari Aina Musdholifah |
| author_facet | Binti Solihah Azhari Azhari Aina Musdholifah |
| author_sort | Binti Solihah |
| collection | DOAJ |
| description | A conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell prediction. In this paper, we compare several conformational epitope B cell prediction models from non-ensemble and ensemble approaches. A sampling method from Random undersampling, SMOTE, and cluster-based undersampling is combined with a decision tree or SVM to build a non-ensemble model. A random forest model and several variants of the bagging method is used to construct the ensemble model. A 10-fold cross-validation method is used to validate the model. The experiment results show that the combination of the cluster-based under-sampling and decision tree outperformed the other sampling method when combined with the non-ensemble and the ensemble method. This study provides a baseline to improve existing models for dealing with the class imbalance in the conformational epitope prediction. |
| first_indexed | 2024-12-16T14:52:54Z |
| format | Article |
| id | doaj.art-6f2d794242f34adc9c7a87c75a25e98c |
| institution | Directory Open Access Journal |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| last_indexed | 2024-12-16T14:52:54Z |
| publishDate | 2021-05-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj.art-6f2d794242f34adc9c7a87c75a25e98c2022-12-21T22:27:32ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012021-05-019113113810.30595/juita.v9i1.99693745The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope PredictionBinti Solihah0Azhari AzhariAina MusdholifahJurusan Teknik Informatika, FTI, Universitas TrisaktiA conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell prediction. In this paper, we compare several conformational epitope B cell prediction models from non-ensemble and ensemble approaches. A sampling method from Random undersampling, SMOTE, and cluster-based undersampling is combined with a decision tree or SVM to build a non-ensemble model. A random forest model and several variants of the bagging method is used to construct the ensemble model. A 10-fold cross-validation method is used to validate the model. The experiment results show that the combination of the cluster-based under-sampling and decision tree outperformed the other sampling method when combined with the non-ensemble and the ensemble method. This study provides a baseline to improve existing models for dealing with the class imbalance in the conformational epitope prediction.http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9969sampling-based method, class imbalance, conformational epitope, b-cell, machine learning-based |
| spellingShingle | Binti Solihah Azhari Azhari Aina Musdholifah The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction Jurnal Informatika sampling-based method, class imbalance, conformational epitope, b-cell, machine learning-based |
| title | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| title_full | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| title_fullStr | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| title_full_unstemmed | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| title_short | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| title_sort | empirical comparison of machine learning algorithm for the class imbalanced problem in conformational epitope prediction |
| topic | sampling-based method, class imbalance, conformational epitope, b-cell, machine learning-based |
| url | http://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/9969 |
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