Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data
The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified sam...
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| Format: | Article |
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MDPI AG
2019-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/6/1476 |
| _version_ | 1817990313422094336 |
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| author | Kewen Li Guangyue Zhou Jiannan Zhai Fulai Li Mingwen Shao |
| author_facet | Kewen Li Guangyue Zhou Jiannan Zhai Fulai Li Mingwen Shao |
| author_sort | Kewen Li |
| collection | DOAJ |
| description | The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances. |
| first_indexed | 2024-04-14T00:57:48Z |
| format | Article |
| id | doaj.art-ce0079984964460d99946c5d74854db2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:57:48Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ce0079984964460d99946c5d74854db22022-12-22T02:21:33ZengMDPI AGSensors1424-82202019-03-01196147610.3390/s19061476s19061476Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced DataKewen Li0Guangyue Zhou1Jiannan Zhai2Fulai Li3Mingwen Shao4College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, ChinaInstitute for Sensing and Embedded Network Systems Engineering, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USASchool of Geosciences, China University of Petroleum, Qingdao 266580, Shandong, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, ChinaThe Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances.https://www.mdpi.com/1424-8220/19/6/1476Adaptive Boostingimbalanced dataArea Under CurveParticle Swarm Optimization |
| spellingShingle | Kewen Li Guangyue Zhou Jiannan Zhai Fulai Li Mingwen Shao Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data Sensors Adaptive Boosting imbalanced data Area Under Curve Particle Swarm Optimization |
| title | Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data |
| title_full | Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data |
| title_fullStr | Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data |
| title_full_unstemmed | Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data |
| title_short | Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data |
| title_sort | improved pso adaboost ensemble algorithm for imbalanced data |
| topic | Adaptive Boosting imbalanced data Area Under Curve Particle Swarm Optimization |
| url | https://www.mdpi.com/1424-8220/19/6/1476 |
| work_keys_str_mv | AT kewenli improvedpsoadaboostensemblealgorithmforimbalanceddata AT guangyuezhou improvedpsoadaboostensemblealgorithmforimbalanceddata AT jiannanzhai improvedpsoadaboostensemblealgorithmforimbalanceddata AT fulaili improvedpsoadaboostensemblealgorithmforimbalanceddata AT mingwenshao improvedpsoadaboostensemblealgorithmforimbalanceddata |