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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Online Access: | 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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my.iium.irep.998262022-09-12T06:42:21Z http://irep.iium.edu.my/99826/ GOS: a Genetic OverSampling Algorithm for classification of Quranic verses Arkok, Bassam Zeki, Akram M. QA76 Computer software TK7885 Computer engineering 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. 2022-06-21 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/99826/2/99826_GOS_A_Genetic_OverSampling_Algorithm_for_Classification_of_Quranic_Verses.pdf Arkok, Bassam and Zeki, Akram M. (2022) GOS: a Genetic OverSampling Algorithm for classification of Quranic verses. In: The 13th International Conference on Information & Communication Systems, 21-23 June 2022, Jordan. http://doi.org/10.1109/ICICS55353.2022.9811224 doi:10.1109/ICICS55353.2022.9811224 |
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QA76 Computer software TK7885 Computer engineering Arkok, Bassam Zeki, Akram M. GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
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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. |
format |
Conference or Workshop Item |
author |
Arkok, Bassam Zeki, Akram M. |
author_facet |
Arkok, Bassam Zeki, Akram M. |
author_sort |
Arkok, Bassam |
title |
GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
title_short |
GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
title_full |
GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
title_fullStr |
GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
title_full_unstemmed |
GOS: a Genetic OverSampling Algorithm for classification of Quranic verses |
title_sort |
gos: a genetic oversampling algorithm for classification of quranic verses |
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2022 |
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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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1744353526031581184 |
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13.19449 |