Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
A Simulation Study to Compare Classification Performance of Fuzzy Inference System with Machine Learning: Methodological Research
Bulanık Çıkarsama Sistemi ile Makine Öğrenmesi Yöntemlerinin Sınıflandırma Performansını Karşılaştıran Bir Simülasyon Çalışması: Metodolojik Araştırma
İrem KARa, Batuhan BAKIRARARa, Derya GÖKMENa, Serdal Kenan KÖSEa
aDepartment of Biostatistics, Ankara University Faculty of Medicine, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2023;15(1):13-8
doi: 10.5336/biostatic.2022-93499
Article Language: EN
Full Text
ABSTRACT
 Objective: Classification is one of the most important research topics of machine learning that aims to correctly predict the target class for each case in the data. In this study, classification performances of fuzzy inference systems that learn from experts and machine learning methods that learn from the data were compared in different sample sizes. Material and Methods: This study was planned as a methodological research. The machine learning algorithms used in the comparison are Multilayer Perceptron, Random Forest, Support Vector Machine, which are frequently encountered classifiers in the literature. The dataset were generated for 6 (sex, chest pain type, max heart rate, exercise induced, oldpeak and major vessels) independent variables determined by variable importance, preserving the characteristics of heart disease data in the University of California Irvine database. Sample sizes were determined in four different sizes as 100, 250, 500 and 1,000. The datasets were divided into two separate sets randomly, with 70% training set and 30% test set. In fuzzy inference systems, the fuzzy rules were automatically generated and Chi's technique was used to create the rules. Accuracy, precision, sensitivity and Fmeasure were used as performance metrics for classification to compare four methods. Results: As a result of the study, it had been observed that fuzzy inference systems are affected by the sample size, and the classification performance is better than other methods as the sample size increases. Conclusion: In general, it has been observed that as the sample size increased, the classification performance of the methods increased.

Keywords: Fuzzy inference system; classification; machine learning
ÖZET
 Amaç: Sınıflandırma, veri setinde her gözlem için hedef sınıfı doğru tahmin etmeyi amaçlayan makine öğrenmesinin en önemli araştırma konularından biridir. Bu çalışmada, uzmandan öğrenen bulanık çıkarsama sistemleri ile veriden öğrenen makine öğrenmesi yöntemlerinin farklı örnek büyüklüklerinde sınıflandırma performansları karşılaştırılmıştır. Gereç ve Yöntemler: Bu çalışma metodolojik bir araştırma olarak planlanmıştır. Karşılaştırmada kullanılan makine öğrenmesi algoritmaları literatürde sıklıkla karşılaşılan sınıflandırıcılar olan; Çok Katmanlı Algılayıcı, Rastgele Orman ve Destek Vektör Makinesi yöntemleridir. Bu çalışmada, University of California Irvine (UCI) makine öğrenmesi veritabanında yer alan kalp hastalığı verisinin özellikleri korunarak değişken önemine göre belirlenen 6 bağımsız değişken (cinsiyet, göğüs ağrı tipi, maksimum kalp atım hızı, egzersizin neden olduğu anjin, oldpeak ve ana damar sayısı) için türetilen veriler kullanılmıştır. Örneklem büyüklükleri 100, 250, 500 ve 1000 olmak üzere dört farklı büyüklükte belirlenmiştir. Veri seti %70 eğitim ve %30 test seti olmak üzere ikiye ayrılmıştır. Bulanık çıkarsama sistemlerinde kurallar otomatik olarak oluşturulmuş olup, kuralların oluşturulmasında Chi'nin tekniğinden yararlanılmıştır. Dört yöntemin karşılaştırılmasında, sınıflandırma performans ölçütleri olarak doğru sınıflama oranı, kesinlik, duyarlılık ve F-ölçütü kullanılmıştır. Bulgular: Çalışma sonucunda, bulanık çıkarsama sistemlerinin örneklem sayısına daha duyarlı olduğu, örneklem büyüklüğü arttıkça sınıflama performansının da diğer yöntemlere göre iyi olduğu gözlenmiştir. Sonuç: Genel olarak ise örneklem büyüklüğü arttıkça, yöntemlerin sınıflama performanslarının arttığı gözlemlenmiştir.

Anahtar Kelimeler: Bulanık çıkarsama sistemleri; sınıflama; makine öğrenmesi
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