Dengesiz Sınıflamada Optimizasyona Dayalı Azörnekleme

Year 2021, Volume: 11 Issue: 2, 385 - 409, 31.12.2021

Fatih Sağlam , Mervenur Sözen , Mehmet Ali Cengiz

https://doi.org/10.37094/adyujsci.884120

Abstract

Sınıflama yöntemleri, sınıf gözlemlerinin sayısı farklı olduğunda çoğunluk sınıfını tahmin etme olasılığının yüksek olduğunu düşünür. Bu sorunu gidermek için literatürde yeniden örnekleme yöntemleri gibi bazı yöntemler bulunmaktadır. Yeniden örnekleme yöntemlerinden biri olan azörnekleme, çoğunluk sınıfından verileri silerek denge oluşturur. Bu çalışma, az örnekleme yapılırken çoğunluk sınıftan alınacak en uygun gözlemleri belirlemek için farklı optimizasyon yöntemlerini karşılaştırmayı amaçlamaktadır. İlk olarak, basit bir simülasyon çalışması yapılmış ve yeniden örneklenen veri setleri arasındaki farklılığı analiz etmek için grafikler kullanılmıştır. Daha sonra, farklı dengesiz veri setleri için farklı sınıflayıcı modelleri oluşturulmuştur. Bu modellerde rastgele azörnekleme, genetik algoritma ile azörnekleme, diferansiyel evrim algoritması ile azörnekleme, yapay arı kolonisi ile azörnekleme ve parçacık sürüsü optimizasyonu ile azörnekleme karşılaştırılmıştır. Sonuçlara sınıflandırıcılara ve veri setlerine göre değişen sıra numaraları verilmiş ve genel bir ortalama sıra elde edilmiştir. Sonuç olarak, yetersiz örnekleme yapıldığında, yapay arı kolonisinin diğer optimizasyon yöntemlerinden daha iyi performans gösterdiği görülmüştür.

Keywords

Dengesiz sınıflar, Sınıflama, Azörnekleme, Optimizasyon

Optimization Based Undersampling for Imbalanced Classes

Abstract

The classification methods consider the probability of predicting the majority class to be high when the number of class observations is different. To address this problem, there are some methods such as resampling methods in the literature. Undersampling, one of the resampling methods, creates balance by removing data from the majority class. This study aims to compare different optimization methods to determine the most suitable observations to be taken from the majority class while undersampling. Firstly, a simple simulation study was conducted and graphs were used to analyze the discrepancy between the resampled datasets. Then, different classifier models were constructed for different imbalanced data sets. In these models, random undersampling, undersampling with genetic algorithm, undersampling with differential evolution algorithm, undersampling with an artificial bee colony, and under-sampling with particle herd optimization were compared. The results were given rank numbers differing depending on the classifiers and data sets and a general mean rank was obtained. As a result, when undersampling, artificial bee colony was seen to perform better than other methods of optimization.

Keywords

Imbalanced classes, Classification, Undersampling, Optimization

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