DENGESİZ KREDİ SKORLAMA VERİ SETLERİNDE KOLEKTİF ÖĞRENME ALGORİTMALARININ PERFORMANS DEĞERLENDİRMESİ

Year 2019, , 180 - 185, 30.07.2019

Nihan Ankara Hulya Sahinturk

https://doi.org/10.17261/Pressacademia.2019.1089

Abstract

Amaç- Kredi skorlama modeli
geliştirilirken kullanılan veri kümelerinde sınıflara ait örneklerin dengesiz
bir dağılıma sahip olmalarından dolayı, modellerin doğruluk oranı  düşük
olmaktadır. Biz bu çalışmada kollektif öğrenme algoritmalarını maliyete duyarlı
öğrenme yöntemiyle birlikte kullanarak elde edilen modellerin performansını
karşılaştıp en etkin modellleri belirlemeye çalıştık. 

Metodoloji- Bu amaçla Bagging ve
AdaBoost kolektif öğrenme yöntemleri karar ağaçları, destek vektör makineleri
ve k-NN temel sınıflandırıcıları ile iki farklı kredi veri seti üzerinde
çalıştırılmıştır. Ayrıca Bagging ve AdaBoost için maliyet duyarlı öğrenme
yöntemi kullanılarak azınlık sınıflandırma grubunun ceza puanı artırılmıştır.  Bütün bu kombinasyonlar kıyaslanmıştır.

Bulgular- Maliyete duyarlı öğrenme
yöntemlerinin kullanılması, hem AdaBoost hem de Bagging için performans
değerlendirme ölçeği AUC açısından daha başarılı sonuçlar elde edilmesini
sağlamıştır. Verideki sınıf dengesizlik oranının artması durumunda,  karar ağaçlarının temel sınıflandırıcı olduğu
Bagging kolektif yönteminin AdaBoost kolektif yöntemine göre daha yüksek başarı
elde ettiği gözlemlenmiştir.

Sonuç- Başarısı yüksek
etkili bir kredi skorlama yöntemi geliştirilmesi hala çözülmesi gereken bir
problem olmasına rağmen kolektif öğrenme yöntemi ile oluşturulan modellerin
bireysel sınıflandırıcılarılarla oluşturulan modellere göre daha yüksek başarı
gösterdiği gözlemlenmiştir.  Bu durum
literatürdeki diğer çalışma bulgularıyla da örtüşmektedir. [Maciej Zięba ve ark., 2012]

Keywords

Kredi skorlama, kolektif öğrenme, dengesiz veri seti

PERFORMANCE EVALUATION OF ENSEMBLE LEARNING ALGORITHMS ON UNBALANCED CREDIT SCORING DATA SETS

Abstract

Purpose- As the credit scoring model
is developed, the accuracy of the models is low due to the unbalanced distribution
of the samples belonging to the classes.
 In this study, we
tried to determine the most effective models by comparing the performance of
the models obtained by using collective learning algorithms together with cost
sensitive learning method.

Methodology- For this purpose,
Bagging and AdaBoost collective learning methods were run on two different
credit data sets with decision trees, support vector machines and k-NN basic
classifiers. In addition, the penal score of the minority classification group
was increased by using cost-sensitive learning method for Bagging and AdaBoost.
All these combinations were compared.

Findings- The use of
cost-sensitive learning methods has led to more successful results in terms of
AUC for both AdaBoost and Bagging. It was observed that the Bagging collective
method, which is the main classifier of decision trees, had higher success than
the AdaBoost collective method in the case of increasing class imbalance rate
in the data.

Conclusion- Although the development of a
highly effective credit scoring method is still a problem that needs to be
solved, it has been observed that the models created by the collective learning
method show higher success than the models created by individual classifiers.
This situation coincides with the findings of other studies in the literature. [Maciej Zięba ve ark., 2012]

Keywords

Credit scoring, ensemble learning, unbalance datasets

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