The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction
| Autor: | Binti Solihah, Azhari Azhari, Aina Musdholifah |
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| Jazyk: | indonéština |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Jurnal Informatika, Vol 9, Iss 1, Pp 131-138 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2086-9398 2579-8901 |
| DOI: | 10.30595/juita.v9i1.9969 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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