journal article·2026-05

Gravitational Dynamic Radius Nearest Neighbor Trained by Fuzzy Enhanced Hiking Optimization Algorithm for Imbalanced Data Classification

Zahra Beheshti

Journal of Bionic Engineering· Volume 23· Issue 3
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OpenAlex topics: Imbalanced Data Classification Techniques · Machine Learning and Data Classification · Artificial Intelligence in Healthcare

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References (43)

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1. ValizadehAslani, T., Shi, Y. W., Wang, J., Ren, P., Zhang, Y., Hu, M., Zhao, L., & Liang, H. L. (2024). Two-stage fine-tuning with ChatGPT data augmentation for learning class-imbalanced data. Neurocomputing, 592, 127801. https://doi.org/10.1016/j.neucom.2024.12780110.1016/j.neucom.2024.127801 ↗
2. Talukder, M. A., Islam, M. M., Uddin, M. A., Hasan, K. F., Sharmin, S., Alyami, S. A., & Moni, M. A. (2024). Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big Data, 11(1), 33. https://doi.org/10.5750/ijme.v167iA2(S).166010.5750/ijme.v167iA2(S).1660 ↗
3. Boyapati, M., & Aygun, R. (2025). BalancerGNN: Balancer graph neural networks for imbalanced datasets: A case study on fraud detection. Neural Networks, 182, 106926. https://doi.org/10.1016/j.neunet.2024.10692610.1016/j.neunet.2024.106926 ↗
4. Jindal, M., & Singh, B. (2024). Transfer learning-based class imbalance-aware shoulder implant classification from x-ray images. Journal of Bionic Engineering, 21(2), 892–912. https://doi.org/10.1007/s42235-023-00477-010.1007/s42235-023-00477-0 ↗
5. Wang, X., Li, Y., & Zhang, J. (2025). Weighted ensemble based on differentiated sampling rates for imbalanced classification and application to credit risk assessment. Expert Systems with Applications, 262, 125595. https://doi.org/10.1016/j.eswa.2024.12559510.1016/j.eswa.2024.125595 ↗
6. Wang, S., Zhang, Y., Piao, X., Lin, X., Hu, Y., & Yin, B. (2025). MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learning. Expert Systems with Applications, 265, 125940. https://doi.org/10.1016/j.eswa.2024.12594010.1016/j.eswa.2024.125940 ↗
7. Ren, J., Wang, Y., Cheung, Y. M., Gao, X. Z., & Guo, X. (2023). Grouping-based oversampling in kernel space for imbalanced data classification. Pattern Recognition, 133, 108992. https://doi.org/10.1016/j.patcog.2022.10899210.1016/j.patcog.2022.108992 ↗
8. Mansouri, M., Nadimi-Shahraki, M. H., & Beheshti, Z. (2025). Swarm-based cost-sensitive decision tree using optimized rules for imbalanced data classification. Journal of Bionic Engineering, 22(3), 1434–1458. https://doi.org/10.1007/s42235-025-00673-010.1007/s42235-025-00673-0 ↗
9. Bach, M., Werner, A., Żywiec, J., & Pluskiewicz, W. (2017). The study of under-and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis. Information Sciences, 384, 174–190. https://doi.org/10.1016/j.ins.2016.09.03810.1016/j.ins.2016.09.038 ↗
10. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321–357. https://doi.org/10.1613/JAIR.95310.1613/JAIR.953 ↗
11. Kanwal, A., Masood, N., Mustafa, G., Ghafoor, M. A., & Ayaz, S. (2025). Mk-smote and m-smote: enhanced techniques for handling class imbalance problem. Iran Journal of Computer Science, 8(2), 627–645. https://doi.org/10.1007/s42044-025-00240-010.1007/s42044-025-00240-0 ↗
12. He, Y., Lu, X., Fournier-Viger, P., & Huang, J. Z. (2024). A novel overlapping minimization SMOTE algorithm for imbalanced classification. Frontiers of Information Technology & Electronic Engineering, 25(9), 1266–1281. https://doi.org/10.1631/FITEE.230027810.1631/FITEE.2300278 ↗
13. Manokaran, J., Vairavel, G., & Vijaya, J. (2024). PPFCM-SMOTE: A novel balancing system for anomaly detection in IoT edge using probabilistic possibilistic fuzzy clustering and SMOTE. International Journal of Information Technology, 1–20. https://doi.org/10.1007/s41870-024-02129-w10.1007/s41870-024-02129-w ↗
14. Mirzaei, B., Nikpour, B., & Nezamabadi-Pour, H. (2021). CDBH: A clustering and density-based hybrid approach for imbalanced data classification. Expert Systems with Applications, 164, 114035. https://doi.org/10.1016/j.eswa.2020.11403510.1016/j.eswa.2020.114035 ↗
15. Naik, S. M., Chakraborty, T., Panja, M., Hadid, A., & Chakraborty, B. (2025). Skew-probabilistic neural networks for learning from imbalanced data. Pattern Recognition, 165, 111677. https://doi.org/10.1016/j.patcog.2025.11167710.1016/j.patcog.2025.111677 ↗
16. Ando, S. (2016). Classifying imbalanced data in distance-based feature space. Knowledge and Information Systems, 46(3), 707–730. https://doi.org/10.1007/s10115-015-0846-310.1007/s10115-015-0846-3 ↗
17. Song, Y., Huang, J., Zhou, D., Zha, H., Giles, C. L., & Iknn (2007). Informative k-nearest neighbor pattern classification. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Berlin, Germany, pp. 248–264. https://doi.org/10.1007/978-3-540-74976-9_2510.1007/978-3-540-74976-9_25 ↗
18. Kriminger, E., Principe, J. C., & Lakshminarayan, C. Nearest neighbor distributions for imbalanced classification. Proceedings of the 2012 international joint conference on neural networks (IJCNN), IEEE, Brisbane, Australia, 2012, 1–5.10.1109/IJCNN.2012.6252718 ↗
19. Zhang, X., Li, Y., Kotagiri, R., Wu, L., Tari, Z., & Cheriet, M. (2017). KRNN: k rare-class nearest neighbour classification. Pattern Recognition, 62, 33–44. https://doi.org/10.1016/j.patcog.2016.08.02310.1016/j.patcog.2016.08.023 ↗
20. Liu, W., & Chawla, S. (2011). Class confidence weighted k NN algorithms for imbalanced data sets. Proceedings of the Pacific-Asia conference on knowledge discovery and data mining, Berlin, Germany, 345–356.10.1007/978-3-642-20847-8_29 ↗
21. Kim, K. (2021). Normalized class coherence change-based kNN for classification of imbalanced data. Pattern Recognition, 120, 108126. https://doi.org/10.1016/j.patcog.2021.10812610.1016/j.patcog.2021.108126 ↗
22. Zhu, Y., Wang, Z., & Gao, D. (2015). Gravitational fixed radius nearest neighbor for imbalanced problem. Knowledge-Based Systems, 90, 224–238. https://doi.org/10.1016/j.knosys.2015.09.01510.1016/j.knosys.2015.09.015 ↗
23. Nikpour, B., Shabani, M., & Nezamabadi-pour, H. (2017). Proposing new method to improve gravitational fixed nearest neighbor algorithm for imbalanced data classification. 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), IEEE, Kerman, Iran, pp. 6–11. https://doi.org/10.1109/CSIEC.2017.794016710.1109/CSIEC.2017.7940167 ↗
24. Shabani-kordshooli, M., Nikpour, B., & Nezamabadi-pour, H. (2017). An improvement to gravitational fixed radius nearest neighbor for imbalanced problem. Proceedings of the 2017 Artificial Intelligence and Signal Processing Conference (AISP), IEEE, Shiraz, Iran, pp. 262–267. https://doi.org/10.1109/AISP.2017.832410910.1109/AISP.2017.8324109 ↗
25. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.00410.1016/j.ins.2009.03.004 ↗
26. Wang, Z., Li, Y., Li, D., Zhu, Z., & Du, W. (2020). Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem. Knowledge-Based Systems, 193, 105474. https://doi.org/10.1016/j.knosys.2020.10547410.1016/j.knosys.2020.105474 ↗
27. Ayodele, A. (2023). A comparative study of ensemble learning techniques for imbalanced classification problems. World Journal of Advanced Research and Reviews, 19(1), 1633–1643. https://doi.org/10.30574/wjarr.2023.19.1.120210.30574/wjarr.2023.19.1.1202 ↗
28. Sun, J., Lang, J., Fujita, H., & Li, H. (2018). Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Information Sciences, 425, 76–91. https://doi.org/10.1016/j.ins.2017.10.01710.1016/j.ins.2017.10.017 ↗
29. Younas, F., Usman, M., & Yan, W. Q. (2023). A deep ensemble learning method for colorectal polyp classification with optimized network parameters. Applied Intelligence, 53(2), 2410–2433. https://doi.org/10.1007/s10489-022-03689-910.1007/s10489-022-03689-9 ↗
30. Du, H., Zhang, Y., Gang, K., Zhang, L., & Chen, Y. C. (2021). Online ensemble learning algorithm for imbalanced data stream. Applied Soft Computing, 107, 107378. https://doi.org/10.1016/j.asoc.2021.10737810.1016/j.asoc.2021.107378 ↗
31. Ding, H., Sun, Y., Wang, Z., Huang, N., Shen, Z., & Cui, X. (2023). RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification. Information Processing & Management, 60(2), 103235. https://doi.org/10.1016/j.ipm.2022.10323510.1016/j.ipm.2022.103235 ↗
32. Beheshti, Z. (2024). A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems. Knowledge-Based Systems, 284, 111191. https://doi.org/10.1016/j.knosys.2023.11119110.1016/j.knosys.2023.111191 ↗
33. Momani, A. A., & Kóczy, L. T. (2025). A robust fingerprint identification approach using a fuzzy system and novel rotation method. Pattern Recognition, 159, 111134. https://doi.org/10.1016/j.patcog.2024.11113410.1016/j.patcog.2024.111134 ↗
34. Oladejo, S. O., Ekwe, S. O., & Mirjalili, S. (2024). The Hiking Optimization Algorithm: A novel human-based metaheuristic approach. Knowledge-Based Systems, 296, 111880. https://doi.org/10.1016/j.knosys.2024.11188010.1016/j.knosys.2024.111880 ↗
35. Muja, M., & Lowe, D. G. (2014). Scalable nearest neighbor algorithms for high dimensional data. IEEE transactions on pattern analysis and machine intelligence, 36(11), 2227–2240. https://doi.org/10.1109/TPAMI.2014.232137610.1109/TPAMI.2014.2321376 ↗
36. Iranmehr, A., Masnadi-Shirazi, H., & Vasconcelos, N. (2019). Cost-sensitive support vector machines. Neurocomputing, 343, 50–64. https://doi.org/10.1016/j.neucom.2018.11.09910.1016/j.neucom.2018.11.099 ↗
37. Cha, X., Deng, L., Yang, Q., & Ling, C. X. (2004). Test-cost sensitive naive bayes classification. Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM’04), Brighton, UK, 51–58.
38. Alcalá-Fdez, J. (2009). KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing, 13(3), 307–318. https://doi.org/10.1007/s00500-008-0323-y10.1007/s00500-008-0323-y ↗
39. Gabor, M. R. (2012). A. new non-parametrical statistics instruments: Friedman test. Theoretical considerations and particularities for marketing data. Proceeding of the 6th International Day in Statistics & Economics, Prague, Czech, 395–403.
40. Memiş, S., Enginoğlu, S., & Erkan, U. (2022). Fuzzy parameterized fuzzy soft k-nearest neighbor classifier. Neurocomputing, 500, 351–378. https://doi.org/10.1016/j.neucom.2022.05.04110.1016/j.neucom.2022.05.041 ↗
41. Kumbure, M. M., & Luukka, P. (2024). Local means-based fuzzy k-nearest neighbor classifier with Minkowski distance and relevance-complementarity feature weighting. Granular Computing, 9(4), 73. https://doi.org/10.1007/s41066-024-00496-010.1007/s41066-024-00496-0 ↗
42. Kumbure, M. M., & Luukka, P. (2022). A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Computing, 7, 657–671. https://doi.org/10.1007/s41066-021-00288-w10.1007/s41066-021-00288-w ↗
43. Kumbure, M. M., Luukka, P., & Collan, M. (2020). A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recognition Letters, 140, 172–178. https://doi.org/10.1016/j.patrec.2020.10.00510.1016/j.patrec.2020.10.005 ↗