Çoklu Doğrusal Bağlantı Olması Durumunda Veri Madenciliği Algoritmaları Performanslarının Karşılaştırılması

Öz

Bilgisayar teknolojilerindeki gelişmelere paralel olarak veri madenciliği algoritmaları ile yapılan çalışmalarda artış yaşanmaktadır. Sınıflandırma algoritmalar ile yapılan çalışmalarda veri kalitesinin bozulması algoritmaların performansında önemli rol oynamaktadır. Bu çalışmada veri kalitesini bozan etmenlerden birisi olan çoklu doğrusal bağlantının veri setinde bulunması durumunda sınıflandırma algoritmalarının performansının nasıl etkilendiği incelenmiştir. Çoklu doğrusal bağlantının varlığını tespit etmek için veri setlerine ait korelasyon grafikleri incelenmiş daha sonrasında ise koşul endeksi ile çoklu doğrusal bağlantının derecesi belirlenmiştir. Sınıflandırma algoritmalarından olan Naive Bayes (NB), Lojistik Regresyon (LR) ve K-En Yakın Komşu Algoritması (kNN), Destek Vektör Makineleri (SVM) ve Aşırı Gradyan Arttırma Algoritması (XGBoost) ile uygulamalar gerçekleştirilmiştir. Yöntemlerin performanslarının incelenmesi için simülasyon çalışması ve gerçek veri setleri ile uygulamalar yapılmış, sonuçlar tablolar halinde sunulmuştur. Analiz sonuçlarına göre, çoklu doğrusal bağlantı varlığında büyük örneklem hacimli veri setlerinde doğruluk ve F-ölçütü metriklerine göre XGBoost algoritmasının diğer algoritmalardan dikkate değer performans farklılığı gösterdiği belirlenmiştir. Çoklu doğrusal bağlantından performansı en olumsuz etkilenen algoritmanın ise Naive Bayes olduğu gözlenmiştir.

Anahtar Kelimeler

• Çoklu doğrusal bağlantı
• Sınıflandırma
• Veri madenciliği

Comparison of Data Mining Algorithms Performances in Case of Multicollinearity

Öz

As advancements in computer technologies progress, there has been an increase in research utilizing data mining algorithms. In studies involving classification algorithms, the degradation of data quality plays a significant role in algorithm performance. This study investigates the impact of multicollinearity, one of the factors that compromise data quality, on the performance of classification algorithms. To identify the presence of multicollinearity, correlation graphs of the datasets were examined, followed by the determination of the degree of multicollinearity using the condition index. The classification algorithms, namely Naive Bayes (NB), Logistic Regression (LR), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost), were implemented for the analysis. Simulation studies and real dataset analyses were conducted to assess the performance of these methods, and the results were presented in tabular form. According to the analysis results, it has been determined that XGBoost algorithm shows a notable performance difference compared to other algorithms in terms of accuracy and F-measure metrics in the presence of multicollinearity in large sample-sized datasets. On the other hand, Naive Bayes was observed to be the algorithm most adversely affected by multicollinearity, showing diminished performance.

Anahtar Kelimeler

• Classification
• data mining
• multicollinearty.

Kaynakça

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