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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12850 |
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| _version_ | 1866909583026421760 |
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| author | Raslan, Khaled SH. Alsharkawy, Almohammady S. Raslan, K. R. |
| author_facet | Raslan, Khaled SH. Alsharkawy, Almohammady S. Raslan, K. R. |
| contents | Classifying imbalanced datasets remains a significant challenge in machine learning, particularly with big data where instances are unevenly distributed among classes, leading to class imbalance issues that impact classifier performance. While Synthetic Minority Over-sampling Technique (SMOTE) addresses this challenge by generating new instances for the under-represented minority class, it faces obstacles in the form of noise and outliers during the creation of new samples. In this paper, a proposed approach, iHHO-SMOTe, which addresses the limitations of SMOTE by first cleansing the data from noise points. This process involves employing feature selection using a random forest to identify the most valuable features, followed by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to detect outliers based on the selected features. The identified outliers from the minority classes are then removed, creating a refined dataset for subsequent oversampling using the hybrid approach called iHHO-SMOTe. The comprehensive experiments across diverse datasets demonstrate the exceptional performance of the proposed model, with an AUC score exceeding 0.99, a high G-means score of 0.99 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.967. These findings collectively establish Cleansed iHHO-SMOTe as a formidable contender in addressing imbalanced datasets, focusing on noise reduction and outlier handling for improved classification models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification Raslan, Khaled SH. Alsharkawy, Almohammady S. Raslan, K. R. Machine Learning Classifying imbalanced datasets remains a significant challenge in machine learning, particularly with big data where instances are unevenly distributed among classes, leading to class imbalance issues that impact classifier performance. While Synthetic Minority Over-sampling Technique (SMOTE) addresses this challenge by generating new instances for the under-represented minority class, it faces obstacles in the form of noise and outliers during the creation of new samples. In this paper, a proposed approach, iHHO-SMOTe, which addresses the limitations of SMOTE by first cleansing the data from noise points. This process involves employing feature selection using a random forest to identify the most valuable features, followed by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to detect outliers based on the selected features. The identified outliers from the minority classes are then removed, creating a refined dataset for subsequent oversampling using the hybrid approach called iHHO-SMOTe. The comprehensive experiments across diverse datasets demonstrate the exceptional performance of the proposed model, with an AUC score exceeding 0.99, a high G-means score of 0.99 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.967. These findings collectively establish Cleansed iHHO-SMOTe as a formidable contender in addressing imbalanced datasets, focusing on noise reduction and outlier handling for improved classification models. |
| title | iHHO-SMOTe: A Cleansed Approach for Handling Outliers and Reducing Noise to Improve Imbalanced Data Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.12850 |