E-postama gönder TİROİD VE KRONİK BÖBREK HASTALIĞI VERİLERİNİN SINIFLANDIRILMASINDA GENETİK ALGORİTMALAR VE PCA İLE HİBRİT ÖZELLİK SEÇİMİ
Öz
TİROİD VE KRONİK BÖBREK HASTALIĞI VERİLERİNİN SINIFLANDIRILMASINDA GENETİK ALGORİTMALAR VE PCA İLE HİBRİT ÖZELLİK SEÇİMİ
Özet
Bu çalışmada tiroid ve kronik böbrek hastalığının teşhisinde k-nearest neighbors sınıflandırıcının performansını arttırmak amacıyla genetik algoritmalar ve temel bileşenler analizi (PCA) hibrit şekilde kullanılmış ve yeni bir özellik seçimi yöntemi önerilmiştir. Hibrit özellik seçimi yönteminde elde edilen uygulama sonuçları, veri setlerinin özellik seçimi uygulanmamış başlangıç performansıyla karşılaştırılmıştır. Sonuç olarak önerilen hibrit metotla birlikte sınıflandırma başarısı tiroid veri seti için %93.44’ten %95.89’a, böbrek veri seti için %93.75’ten %98.25’e çıkarılmıştır. Sonuçların tutarlı olması için her iki veri setine 10-kat çapraz doğrulama yapılmıştır.
Anahtar Kelimeler: Genetik Algoritmalar, PCA, Özellik Seçimi, K-nearest neighbors
HYBRID FEATURE SELECTION USING GENETIC ALGORITHMS AND PCA IN CLASSIFICATION OF THYROID AND CHRONIC KIDNEY DISEASE DATA
Abstract
In this study, genetic algorithms and principal component analysis (PCA) were used in a hybrid way to increase the performance of the k-nearest neighbors classifier in the diagnosis of thyroid and chronic kidney disease, and a new feature selection method was proposed. The application results obtained in the hybrid feature selection method were compared with the initial performance of the data sets before the feature selection was applied. As a result, with the proposed hybrid method, the classification success was increased from 93.44% to 95.89% for the thyroid data set and from 93.75%to 98.25%for the kidney data set. A 10-fold cross validation was applied to both data sets to ensure consistent results.
Keywords: Genetic Algorithms, PCA, Feature Selection, K-nearest neighbors
Anahtar Kelimeler
Tam Metin:
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