Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models
In the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta...
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MDPI AG
2025-01-01
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| author | Ruinan Qiu Yongfeng Yin Qingran Su Tianyi Guan |
| author_facet | Ruinan Qiu Yongfeng Yin Qingran Su Tianyi Guan |
| author_sort | Ruinan Qiu |
| collection | DOAJ |
| description | In the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta-learning strategies, taking advantage of the diversity of heterogeneous classifiers. However, the fixed weight distribution strategy in traditional bagging methods often has limitations when handling complex or imbalanced datasets. This paper combines the concept of heterogeneous classifier integration in stacking with the weighted averaging strategy of bagging, proposing a new adaptive weight distribution approach to enhance bagging’s performance in heterogeneous ensemble settings. Specifically, we propose three weight generation functions with “high at both ends, low in the middle” curve shapes and demonstrate the superiority of this strategy over fixed weight methods on two datasets. Additionally, we design a specialized neural network, and by training it adequately, validate the rationality of the proposed adaptive weight distribution strategy, further improving the model’s robustness. The above methods are collectively called func-bagging. Experimental results show that func-bagging has an average 1.810% improvement in extreme performance compared to the base classifier, and is superior to stacking and bagging methods. It also has better dataset adaptability and interpretability than stacking and bagging. Therefore, func-bagging is particularly effective in scenarios with class imbalance and is applicable to classification tasks with imbalanced classes, such as anomaly detection. |
| format | Article |
| id | doaj-art-43c9da7fdc274f3bb5456e82d688f062 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-43c9da7fdc274f3bb5456e82d688f0622025-01-24T13:21:17ZengMDPI AGApplied Sciences2076-34172025-01-0115290510.3390/app15020905Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection ModelsRuinan Qiu0Yongfeng Yin1Qingran Su2Tianyi Guan3School of Software, Beihang University, Haidian, Beijing 100191, ChinaSchool of Software, Beihang University, Haidian, Beijing 100191, ChinaSchool of Computer Science and Engineering, Beihang University, Haidian, Beijing 100191, ChinaSchool of Software, Beihang University, Haidian, Beijing 100191, ChinaIn the field of ensemble learning, bagging and stacking are two widely used ensemble strategies. Bagging enhances model robustness through repeated sampling and weighted averaging of homogeneous classifiers, while stacking improves classification performance by integrating multiple models using meta-learning strategies, taking advantage of the diversity of heterogeneous classifiers. However, the fixed weight distribution strategy in traditional bagging methods often has limitations when handling complex or imbalanced datasets. This paper combines the concept of heterogeneous classifier integration in stacking with the weighted averaging strategy of bagging, proposing a new adaptive weight distribution approach to enhance bagging’s performance in heterogeneous ensemble settings. Specifically, we propose three weight generation functions with “high at both ends, low in the middle” curve shapes and demonstrate the superiority of this strategy over fixed weight methods on two datasets. Additionally, we design a specialized neural network, and by training it adequately, validate the rationality of the proposed adaptive weight distribution strategy, further improving the model’s robustness. The above methods are collectively called func-bagging. Experimental results show that func-bagging has an average 1.810% improvement in extreme performance compared to the base classifier, and is superior to stacking and bagging methods. It also has better dataset adaptability and interpretability than stacking and bagging. Therefore, func-bagging is particularly effective in scenarios with class imbalance and is applicable to classification tasks with imbalanced classes, such as anomaly detection.https://www.mdpi.com/2076-3417/15/2/905ensemble learningbaggingstackingadaptive weight generationanomaly detection |
| spellingShingle | Ruinan Qiu Yongfeng Yin Qingran Su Tianyi Guan Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models Applied Sciences ensemble learning bagging stacking adaptive weight generation anomaly detection |
| title | Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models |
| title_full | Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models |
| title_fullStr | Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models |
| title_full_unstemmed | Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models |
| title_short | Func-Bagging: An Ensemble Learning Strategy for Improving the Performance of Heterogeneous Anomaly Detection Models |
| title_sort | func bagging an ensemble learning strategy for improving the performance of heterogeneous anomaly detection models |
| topic | ensemble learning bagging stacking adaptive weight generation anomaly detection |
| url | https://www.mdpi.com/2076-3417/15/2/905 |
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