(Universitas Medan Area, Indonesia)(2) Muhammad Khahfi Zuhanda
(Universitas Medan Area, Indonesia)(3) Rahmad Syah
(Universitas Medan Area, Indonesia)*corresponding author
AbstractClass imbalance remains a significant challenge in classification tasks, particularly when the minority class exhibits complex internal structures. This study proposes a unified framework that integrates Incremental SMOTE-KMeans with a cluster-structured positive class-SVM (CS-PC-SVM) to jointly address data imbalance and structural heterogeneity. The proposed method introduces structural alignment between oversampling and classification by generating synthetic samples only within reliable clusters and modeling the minority class as multiple subgroups. This design reduces noise, preserves local data structure, and enables more adaptive decision boundaries. Experimental results on five benchmark datasets demonstrate that the proposed approach achieves consistently strong and balanced performance, with Accuracy up to 0.981, G-Mean 0.969, Precision 0.967, and Recall 0.962, outperforming or remaining competitive with existing methods. These findings highlight the effectiveness of integrating structure-guided data generation with structure-aware classification for improving robustness in imbalanced learning scenarios.
KeywordsClass Imbalance; Incremental SMOTE-KMeans; Cluster-Structured Positive Class SVM; Classification; Accuracy
|
DOIhttps://doi.org/10.26555/ijain.v12i3.2432 |
Article metricsAbstract views : 214 | PDF views : 5 |
Cite |
Full Text Download
|
References
[1] N. U. Niaz, K. M. N. Shahariar, and M. J. A. Patwary, “Class Imbalance Problems in Machine Learning: A Review of Methods and Future Challenges,” in Proceedings of the 2nd International Conference on Computing Advancements, Mar. 2022, pp. 485–490, doi: 10.1145/3542954.3543024.
[2] J. Villanueva-García, I. Moral-Arce, and L. J. García Villalba, “A cumulative, pipeline-oriented framework for extreme imbalanced classification: Large-scale benchmarking of 4,100 rare-event models,” Expert Syst. Appl., vol. 312, p. 131366, May 2026, doi: 10.1016/j.eswa.2026.131366.
[3] K. Ghosh, C. Bellinger, R. Corizzo, P. Branco, B. Krawczyk, and N. Japkowicz, “The class imbalance problem in deep learning,” Mach. Learn., vol. 113, no. 7, pp. 4845–4901, Jul. 2024, doi: 10.1007/s10994-022-06268-8.
[4] J. Jiang et al., “A review of machine learning methods for imbalanced data challenges in chemistry,” Chem. Sci., vol. 16, no. 18, pp. 7637–7658, 2025, doi: 10.1039/D5SC00270B.
[5] S. Feng, J. Keung, X. Yu, Y. Xiao, and M. Zhang, “Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction,” Inf. Softw. Technol., vol. 139, p. 106662, Nov. 2021, doi: 10.1016/j.infsof.2021.106662.
[6] L. Hu and Y. Zhang, “GDSMOTE: A Novel Synthetic Oversampling Method for High-Dimensional Imbalanced Financial Data,” Mathematics, vol. 12, no. 24, p. 4036, Dec. 2024, doi: 10.3390/math12244036.
[7] T. Kosolwattana, C. Liu, R. Hu, S. Han, H. Chen, and Y. Lin, “A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare,” BioData Min., vol. 16, no. 1, p. 15, Apr. 2023, doi: 10.1186/s13040-023-00330-4.
[8] A. F. Kalay, “Generating Synthetic Data With Locally Estimated Distributions for Disclosure Control,” Aust. N. Z. J. Stat., vol. 68, no. 1, p. e70032, Mar. 2026, doi: 10.1111/anzs.70032.
[9] I. M. Alkhawaldeh, I. Albalkhi, and A. J. Naswhan, “Challenges and limitations of synthetic minority oversampling techniques in machine learning,” World J. Methodol., vol. 13, no. 5, pp. 373–378, Dec. 2023, doi: 10.5662/wjm.v13.i5.373.
[10] H. Nizam‐Ozogur and Z. Orman, “A heuristic-based hybrid sampling method using a combination of SMOTE and ENN for imbalanced health data,” Expert Syst., vol. 41, no. 8, pp. 1–22, Aug. 2024, doi: 10.1111/exsy.13596.
[11] C. Bunkhumpornpat, E. Boonchieng, V. Chouvatut, and D. Lipsky, “FLEX-SMOTE: Synthetic over-sampling technique that flexibly adjusts to different minority class distributions,” Patterns, vol. 5, no. 11, p. 101073, Nov. 2024, doi: 10.1016/j.patter.2024.101073.
[12] X. Lu, X. Ye, and Y. Cheng, “An overlapping minimization-based over-sampling algorithm for binary imbalanced classification,” Eng. Appl. Artif. Intell., vol. 133, p. 108107, Jul. 2024, doi: 10.1016/j.engappai.2024.108107.
[13] G. Husain et al., “SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models,” Algorithms, vol. 18, no. 1, p. 37, Jan. 2025, doi: 10.3390/a18010037.
[14] S. Song, S. Yang, and M. Sun, “CS-SMOTE: An Improved Oversampling Method Combining SMOTE Method and Symmetrical Cube Scoring Mechanism,” Symmetry (Basel)., vol. 17, no. 11, p. 1941, Nov. 2025, doi: 10.3390/sym17111941.
[15] L. Tao, H. Li, F. Wang, M. Liu, Z. Tang, and Q. Wang, “An Adaptive Safe-Region Diversity Oversampling Algorithm for Imbalanced Classification,” IEEE Access, vol. 12, pp. 63713–63724, 2024, doi: 10.1109/ACCESS.2024.3396155.
[16] A. Abernathy and M. E. Celebi, “The incremental online k-means clustering algorithm and its application to color quantization,” Expert Syst. Appl., vol. 207, p. 117927, Nov. 2022, doi: 10.1016/j.eswa.2022.117927.
[17] D. S. Turan and B. Ordin, “The incremental SMOTE: A new approach based on the incremental k-means algorithm for solving imbalanced data set problem,” Inf. Sci. (Ny)., vol. 711, p. 122103, Sep. 2025, doi: 10.1016/j.ins.2025.122103.
[18] A. Cabras, P. Ortu, T. Pisanu, P. Maxia, and R. Caocci, “Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy,” Sensors, vol. 24, no. 7, p. 2278, Apr. 2024, doi: 10.3390/s24072278.
[19] A. Zhu, Z. Hua, Y. Shi, Y. Tang, and L. Miao, “An Improved K-Means Algorithm Based on Evidence Distance,” Entropy, vol. 23, no. 11, p. 1550, Nov. 2021, doi: 10.3390/e23111550.
[20] K. R. Mahmudah, F. Indriani, Y. Takemori-Sakai, Y. Iwata, T. Wada, and K. Satou, “Classification of Imbalanced Data Represented as Binary Features,” Appl. Sci., vol. 11, no. 17, p. 7825, Aug. 2021, doi: 10.3390/app11177825.
[21] M. G. Malhat, A. M. Elsobky, A. E. Keshk, H. A. Abdallah, and M. Hussein, “An approach for handling imbalanced datasets using borderline shifting,” Sci. Rep., vol. 16, no. 1, p. 8264, Mar. 2026, doi: 10.1038/s41598-026-39118-x.
[22] D. Elreedy, A. F. Atiya, and F. Kamalov, “A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning,” Mach. Learn., vol. 113, no. 7, pp. 4903–4923, Jul. 2024, doi: 10.1007/s10994-022-06296-4.
[23] J. Klikowski and M. Woźniak, “Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification,” 2020, pp. 117–127, doi: 10.1007/978-3-030-50423-6_9.
[24] H. Najafi, G. L. Lagerwall, J. Obeysekera, and J. Liu, “Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Physical Consistency, and Operational Applications,” Water, vol. 18, no. 2, p. 271, Jan. 2026, doi: 10.3390/w18020271.
[25] P. Vuttipittayamongkol and E. Elyan, “Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets,” 2020, pp. 358–369, doi: 10.1007/978-3-030-49186-4_30.
[26] Y. Tao, Y. Zhang, and B. Jiang, “DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction,” BMC Med. Genomics, vol. 13, no. S10, p. 152, Oct. 2020, doi: 10.1186/s12920-020-00781-2.
[27] P. Cao, D. Zhao, and O. Zaiane, “An Optimized Cost-Sensitive SVM for Imbalanced Data Learning,” 2013, pp. 280–292, doi: 10.1007/978-3-642-37456-2_24.
[28] A. X. Wang, V.-T. Le, H. N. Trung, and B. P. Nguyen, “Addressing imbalance in health data: Synthetic minority oversampling using deep learning,” Comput. Biol. Med., vol. 188, p. 109830, Apr. 2025, doi: 10.1016/j.compbiomed.2025.109830.
[29] C. Shyalika, R. Wickramarachchi, and A. P. Sheth, “A Comprehensive Survey on Rare Event Prediction,” ACM Comput. Surv., vol. 57, no. 3, pp. 1–39, Mar. 2025, doi: 10.1145/3699955.
[30] P. Soltanzadeh, M. R. Feizi-Derakhshi, and M. Hashemzadeh, “Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach,” Pattern Recognit., vol. 143, p. 109721, Nov. 2023, doi: 10.1016/j.patcog.2023.109721.
[31] J. Huang, Y. Wang, and M. Wang, “Class-Adaptive Weighted Broad Learning System with Hybrid Memory Retention for Online Imbalanced Classification,” Electronics, vol. 14, no. 17, p. 3562, Sep. 2025, doi: 10.3390/electronics14173562.
[32] Y. Li, Y. Yang, P. Song, L. Duan, and R. Ren, “An improved SMOTE algorithm for enhanced imbalanced data classification by expanding sample generation space,” Sci. Rep., vol. 15, no. 1, p. 23521, Jul. 2025, doi: 10.1038/s41598-025-09506-w.
[33] J. Lyu, J. Yang, Z. Su, and Z. Zhu, “LD-SMOTE: A Novel Local Density Estimation-Based Oversampling Method for Imbalanced Datasets,” Symmetry (Basel)., vol. 17, no. 2, p. 160, Jan. 2025, doi: 10.3390/sym17020160.
[34] Y. Pristyanto, A. F. Nugraha, A. Dahlan, L. A. Wirasakti, A. Ahmad Zein, and I. Pratama, “Multiclass Imbalanced Handling using ADASYN Oversampling and Stacking Algorithm,” in 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), Jan. 2022, pp. 1–5, doi: 10.1109/IMCOM53663.2022.9721632.
[35] A. R. Salehi and M. Khedmati, “A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data,” Sci. Rep., vol. 14, no. 1, p. 5152, Mar. 2024, doi: 10.1038/s41598-024-55598-1.
[36] C. Jimenez-Castaño, A. Álvarez-Meza, D. Cárdenas-Peña, A. Orozco-Gutíerrez, and J. Guerrero-Erazo, “Kreĭn twin support vector machines for imbalanced data classification,” Pattern Recognit. Lett., vol. 182, pp. 39–45, Jun. 2024, doi: 10.1016/j.patrec.2024.03.017.
[37] A. Salehi and M. Khedmati, “Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification,” Sci. Rep., vol. 15, no. 1, p. 3460, Jan. 2025, doi: 10.1038/s41598-024-84786-2.
[38] G. A. Pradipta, R. Wardoyo, A. Musdholifah, I. N. H. Sanjaya, and M. Ismail, “SMOTE for Handling Imbalanced Data Problem : A Review,” in 2021 Sixth International Conference on Informatics and Computing (ICIC), Nov. 2021, pp. 1–8, doi: 10.1109/ICIC54025.2021.9632912.
[39] Z. Hengyu, “A New Cost-sensitive SVM Algorithm for Imbalanced Dataset,” in 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), Jan. 2021, pp. 402–407, doi: 10.1109/ICCECE51280.2021.9342072.
[40] J. Mun, S. Bang, and J. Kim, “Weighted support vector machine for extremely imbalanced data,” Comput. Stat. Data Anal., vol. 203, p. 108078, Mar. 2025, doi: 10.1016/j.csda.2024.108078.
[41] J. Guo, H. Wu, X. Chen, and W. Lin, “Adaptive SV-Borderline SMOTE-SVM algorithm for imbalanced data classification,” Appl. Soft Comput., vol. 150, p. 110986, Jan. 2024, doi: 10.1016/j.asoc.2023.110986.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0