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A Hybrid Technique for Detection and Handling Noise in Binary Classification

Cilt: 30 Sayı: 2 31 Ağustos 2025
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A Hybrid Technique for Detection and Handling Noise in Binary Classification

Öz

Binary classification is a widely utilized method in data mining. However, the presence of noise within the training dataset can significantly impact classification accuracy. Our aim in this study is to identify such noisy data by using polyhedral conic functions. Then the dataset is reconstructed by making the necessary changes to enhance the effectiveness of binary classification studies by improving the quality of the training data.

Anahtar Kelimeler

Binary classification, Data mining, Mathematical optimization, Noisy data detection, Polyhedral conic functions

Kaynakça

  1. Astorino, A., Gaudioso, M., & Seeger, A. (2014). Conic separation of finite sets I. The homogeneous case. Journal of Convex Analysis, 21(1), 1-28.
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  3. Blaszczynski, J., & Stefanowski, J. (2015). Neighbourhood sampling in bagging for imbalanced data. Neurocomputing, 150, 529-542. https://doi.org/10.1016/j.neucom.2014.07.064
  4. Bramer, M. (2020). Principles of data mining. Springer. https://doi.org/10.1007/978-1-4471-7493-6
  5. Brodley, C. E., & Friedl, M. A. (1999). Identifying mislabeled training data. Journal of Artificial Intelligence Research, 11, 131–167. https://doi.org/ 10.1613/jair.606
  6. Gasimov, R. N., & Ozturk, G. (2006). Separation via polyhedral conic functions. Optimization Methods and Software, 21(4), 527-540. https://doi.org/10.1080/10556780600723252
  7. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178-210. https://doi.org/10.1016/j.ins.2022.11.139
  8. Karimi, D., Dou, H., Warfield, S. K., & Gholi, A. (2020). Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Medical Image Analysis, 65, 101759. https://doi.org/10.1016/j.media.2020.101759
  9. Kasimbeyli, R. (2010). A nonlinear cone separation theorem and scalarization in nonconvex vector optimization. SIAM Journal on Optimization, 20(3), 1591-1619. https://doi.org/10.1137/070694089
  10. Kelly, M., Longjohn, R., & Nottingham, K. (2023). UCI machine learning repository, University of California, School of Information and Computer Science. Erişim tarihi: 28.01.2025. http://archive.ics.uci.edu/ml

Kaynak Göster

APA
Uylaş Satı, N. (2025). A Hybrid Technique for Detection and Handling Noise in Binary Classification. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 30(2), 584-595. https://doi.org/10.53433/yyufbed.1632877
AMA
1.Uylaş Satı N. A Hybrid Technique for Detection and Handling Noise in Binary Classification. YYUFBED. 2025;30(2):584-595. doi:10.53433/yyufbed.1632877
Chicago
Uylaş Satı, Nur. 2025. “A Hybrid Technique for Detection and Handling Noise in Binary Classification”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30 (2): 584-95. https://doi.org/10.53433/yyufbed.1632877.
EndNote
Uylaş Satı N (01 Ağustos 2025) A Hybrid Technique for Detection and Handling Noise in Binary Classification. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30 2 584–595.
IEEE
[1]N. Uylaş Satı, “A Hybrid Technique for Detection and Handling Noise in Binary Classification”, YYUFBED, c. 30, sy 2, ss. 584–595, Ağu. 2025, doi: 10.53433/yyufbed.1632877.
ISNAD
Uylaş Satı, Nur. “A Hybrid Technique for Detection and Handling Noise in Binary Classification”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi 30/2 (01 Ağustos 2025): 584-595. https://doi.org/10.53433/yyufbed.1632877.
JAMA
1.Uylaş Satı N. A Hybrid Technique for Detection and Handling Noise in Binary Classification. YYUFBED. 2025;30:584–595.
MLA
Uylaş Satı, Nur. “A Hybrid Technique for Detection and Handling Noise in Binary Classification”. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 30, sy 2, Ağustos 2025, ss. 584-95, doi:10.53433/yyufbed.1632877.
Vancouver
1.Nur Uylaş Satı. A Hybrid Technique for Detection and Handling Noise in Binary Classification. YYUFBED. 01 Ağustos 2025;30(2):584-95. doi:10.53433/yyufbed.1632877