ACADSTAFF UGM
| Title | : | The Empirical Comparison of Machine Learning Algorithm for the Class Imbalanced Problem in Conformational Epitope Prediction |
| Author | : |
Binti Solihah (1) Prof. Dr. Drs. Azhari, MT. (2) Aina Musdholifah, S.Kom., M.Kom. Ph.D (3) |
| Date | : | 0 2021 |
| Keyword | : | sampling-based method,class imbalance,conformational epitope,B-cell,machine learning-based sampling-based method,class imbalance,conformational epitope,B-cell,machine learning-based |
| Abstract | : | 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. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Nasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
The Empirical Comparison of Machine Learning Algorith for the Class Imbalanced___.pdf
Document Type : [PAK] Full Dokumen
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