Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction
Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space si...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9851666/ |
| _version_ | 1811319841995358208 |
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| author | Nazeeh Ghatasheh Ismail Altaharwa Khaled Aldebei |
| author_facet | Nazeeh Ghatasheh Ismail Altaharwa Khaled Aldebei |
| author_sort | Nazeeh Ghatasheh |
| collection | DOAJ |
| description | Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms <inline-formula> <tex-math notation="LaTeX">$Chi^{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$PCA$ </tex-math></inline-formula> feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works. |
| first_indexed | 2024-04-13T12:50:24Z |
| format | Article |
| id | doaj.art-9cb56f6c1a6f4cabbb12b5fae0e2bca2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T12:50:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9cb56f6c1a6f4cabbb12b5fae0e2bca22022-12-22T02:46:15ZengIEEEIEEE Access2169-35362022-01-0110843658438310.1109/ACCESS.2022.31969059851666Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam PredictionNazeeh Ghatasheh0https://orcid.org/0000-0002-8000-0910Ismail Altaharwa1https://orcid.org/0000-0001-8775-0581Khaled Aldebei2https://orcid.org/0000-0001-6385-1134Department of Information Technology, The University of Jordan, Aqaba, JordanDepartment of Computer Information Systems, The University of Jordan, Aqaba, JordanDepartment of Information Technology, The University of Jordan, Aqaba, JordanRecently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms <inline-formula> <tex-math notation="LaTeX">$Chi^{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$PCA$ </tex-math></inline-formula> feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.https://ieeexplore.ieee.org/document/9851666/Genetic algorithmbusiness analyticsextreme gradient boostingfeature selectionhyper parameter optimizationspam prediction |
| spellingShingle | Nazeeh Ghatasheh Ismail Altaharwa Khaled Aldebei Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction IEEE Access Genetic algorithm business analytics extreme gradient boosting feature selection hyper parameter optimization spam prediction |
| title | Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction |
| title_full | Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction |
| title_fullStr | Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction |
| title_full_unstemmed | Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction |
| title_short | Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction |
| title_sort | modified genetic algorithm for feature selection and hyper parameter optimization case of xgboost in spam prediction |
| topic | Genetic algorithm business analytics extreme gradient boosting feature selection hyper parameter optimization spam prediction |
| url | https://ieeexplore.ieee.org/document/9851666/ |
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