A Hybrid Technique for Detection and Handling Noise in Binary Classification

Year 2025, Volume: 30 Issue: 2, 584 - 595, 31.08.2025

Nur Uylaş Satı

https://doi.org/10.53433/yyufbed.1632877

Abstract

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.

Keywords

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

İkili Sınıflandırmada Gürültünün Algılanması ve Ele Alınması için Hibrit Bir Teknik

Abstract

İkili sınıflandırma, günümüzde veri madenciliği araştırmacıları tarafından sıkça kullanılan yöntemlerden birisidir. Bu yöntemde, eğitim kümesindeki verilerde saklı gürültü doğruluk değerini önemli ölçüde etkilemektedir. Bu çalışmada amacımız bu gürültü verilerini çok yüzlü konik fonksiyonlar yardımıyla belirlemek ve sonrasında gerekli değişiklikleri yaparak yeniden oluşturduğumuz eğitim veri kümesi ile ikili sınıflandırma çalışmalarında daha etkili sonuçlar elde etmektir.

Keywords

Çok yüzlü konik fonksiyonlar , Gürültülü veri tespiti , İkili sınıflandırma , Matematiksel optimizasyon , Veri madenciliği

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