A new binary classifier robust on noisy domains based on kNN algorithm

Year 2024, Volume: 17 Issue: 4, 309 - 321, 31.10.2024

Müge Acar

https://doi.org/10.17671/gazibtd.1534334

Abstract

Classification is an effective technique commonly used in data analysis by systematically arranging groups or categories according to established criteria. The classifier's success relies on the classifier itself and the quality of the data. However, in real-world applications, it is inevitable for datasets to contain mislabeled instances, which may cause misclassification challenges that classifiers have to handle. This study aims for a quantitative assessment of the classification of noisy data through a new kNN-based classification algorithm and to increase the performance of classical kNN by efficiently classifying the data. We perform various numerical experiments on real-world data sets to prove our new algorithm's performance. We obtain high standards of accuracy levels on various noisy datasets. We propose that this new technique can provide high standard accuracy levels in binary classification problems. We compared the new kNN and classical kNN algorithms in various noise levels (10%, 20%, 30%, and 40%) on distinct datasets by measuring in terms of test accuracy. Also, we compared our new algorithm with popular classification algorithms and in the vast majority, we obtained better test accuracy results.

Keywords

Binary classification, Noisy data, Data Mining

Gürültülü Verilere Dayanıklı kNN Algoritması Temelli Yeni Bir İkili Sınıflandırma Algoritması

Abstract

Sınıflandırma, belirlenmiş bazı kriterlere göre kategoriler halinde sistematik olarak verilerin analizinde kullanılan etkili bir tekniktir. Sınıflandırıcının başarısı, sınıflandırıcının kendisine ve verilerin kalitesine bağlıdır. Bununla birlikte, gerçek hayat uygulamalarında, veri kümelerinin yanlış etiketlenmiş örnekler içermesi kaçınılmazdır. Gerçek hayat verileri gürültü olarak bilinen yanlış etiketlenmiş örnekler içerebilir. Bu da yanlış sınıflandırmalara neden olabilir. Bu çalışma, yeni bir kNN (k en yakın komşuluk algortiması) tabanlı sınıflandırma algoritması ile gürültü verilerinin sınıflandırılmasının nicel bir değerlendirmesini ve verileri verimli bir şekilde sınıflandırarak klasik kNN'nin performansını artırmayı amaçlamaktadır. Bu yeni tekniğin, gürültü verileriyle ikili sınıflandırma problemlerinde yüksek standart doğruluk seviyeleri sağlayabileceğini önermekteyiz. Bu çalışma, sınıflandırmadan önce gürültü noktaları tespit edilmesini dikkate alarak ikili sınıflandırma problemlerinde kNN tekniğinin performansını arttırabilmektedir. Yeni kNN ve klasik kNN algoritmalarını farklı gürültü seviyelerinde (%10, %20, %30 ve %40) farklı veri setlerinde test doğruluğu açısından ölçerek karşılaştırıldı ve başarılı sonuçlar elde edildi. Ayrıca yeni algoritma popüler sınıflandırma algoritmalarıyla karşılaştırıldı ve daha iyi test doğruluğu sonuçları elde edildi.

Keywords

İkili Sınıflandırma, Gürültü Verisi, Veri Madenciliği

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