GOS: a Genetic OverSampling Algorithm for classification of Quranic verses
Imbalanced classes problem is a problem in many datasets in real applications, where one class “minority class" contains few numbers of samples and the other "majority class" contains many numbers of samples. It is difficult to build a training model to classify the imbalanced classes...
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| Main Authors: | , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2022
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| 主題: | |
| 在線閱讀: | http://irep.iium.edu.my/99826/2/99826_GOS_A_Genetic_OverSampling_Algorithm_for_Classification_of_Quranic_Verses.pdf http://irep.iium.edu.my/99826/ http://doi.org/10.1109/ICICS55353.2022.9811224 |
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| 總結: | Imbalanced classes problem is a problem in many datasets in real applications, where one class “minority class" contains few numbers of samples and the other "majority class" contains many numbers of samples. It is difficult to build a training model to classify the imbalanced classes correctly due to tending the accuracy of the classification of the majority class. In this paper, a new technique called "GOS: a Genetic OverSampling algorithm”, is proposed using a genetic algorithm. A genetic algorithm is applied to oversample the imbalanced datasets and to improve the performance of imbalanced classification. This improvement is achieved due to adjusting the locations of samples in the minority class in the optimal places. According to the experimental results obtained, the GOS algorithm outperformed other techniques used widely in the imbalanced classification field. |
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