Weighted neural network for imbalanced information with undersampling

Alaba S. Y., Siddique N. A., BENLİ E., Enns D., Motai Y.

Array, cilt.29, 2026 (ESCI, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.array.2025.100668
  • Dergi Adı: Array
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Compendex, Directory of Open Access Journals
  • Anahtar Kelimeler: Artificial neural network, Highly imbalanced data classification, Kernel adjustment, Pattern classification, Supervised classification, Undersampling
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Class imbalance presents a significant challenge in machine learning, particularly in applications where accurate detection of minority classes is essential. This study presents an innovative method for precisely and sensitively classifying imbalanced data using a feed-forward artificial neural network architecture. By addressing the limitations of conventional linear models, which often overlook minority classes, the proposed method combines selective undersampling, adaptive kernel optimization, and a weighted neural network structure. The training dataset is iteratively refined to prioritize minority classes, while a kernel function optimizer sharpens class boundaries through adaptive transformations. Experimental results demonstrate that the proposed model consistently outperforms standard techniques on diverse imbalanced datasets, achieving high accuracy (above 90 %), sensitivity, and G-mean scores (above 80 %). Computational efficiency analysis further strengthens the method's practicality for real-world applications by balancing classification precision with processing efficiency.