TY  - JOUR
T1  - Bireylerin Çevresel Tutumlarını Tahminde Makine Öğrenmesi: ANOVA ve Ki-Kare Temelli Özellik Seçimi ile Algoritma Performanslarının Karşılaştırılması
TT  - Predicting Individuals' Environmental Attitudes Using Machine Learning: Comparison of Algorithm Performances with ANOVA and Chi-Square-Based Feature
AU  - Şenyer Yapıcı, İrem
AU  - Uzun Arslan, Rukiye
AU  - Alkan, Fuat
PY  - 2025
DA  - May
Y2  - 2025
JF  - EMO Bilimsel Dergi
PB  - TMMOB Elektrik Mühendisleri Odası
WT  - DergiPark
SN  - 1309-5501
SP  - 61
EP  - 69
VL  - 15
IS  - 2
LA  - tr
AB  - Bu çalışma, bireylerin çevresel tutumlarının makine öğrenmesi (MÖ) yöntemleriyle tahmin edilmesine yönelik veri odaklı bir model geliştirmeyi amaçlamaktadır. Çalışmada, çevresel farkındalık düzeylerinin sınıflandırılmasına yönelik beş farklı MÖ algoritmasının (Destek Vektör Makineleri, Gradyan Artırma (GA), Çok Katmanlı Algılayıcı, Kuadratik Diskriminant Analizi ve Torbalama) performansı karşılaştırılmıştır. Model oluşturma sürecinde, veri setindeki değişkenlerin sınıflandırma başarısına etkisini belirlemek amacıyla tek yönlü varyans analizi (ANOVA) ve Ki-Kare Bağımsızlık Testi gibi istatistiksel yöntemler uygulanmıştır.Deneysel sonuçlar, ANOVA ve Ki-Kare tabanlı özellik seçimi süreçlerinin model başarımını artırmada etkili olduğunu göstermektedir. Özellikle GA algoritması, doğruluk, kesinlik ve F1 skoru bakımından diğer yöntemlere kıyasla üstün performans sergilemiştir. Elde edilen bulgular, MÖ algoritmalarının çevresel tutumların modellenmesi ve tahmin edilmesinde güçlü bir analitik çerçeve sunduğunu ortaya koymaktadır. Çalışma, çevresel farkındalık düzeylerinin veri odaklı yöntemlerle değerlendirilmesinin, sürdürülebilir çevre politikalarının geliştirilmesine ve bireylerin çevre bilincinin artırılmasına katkı sağlayacağını ortaya koymaktadır.
KW  - çevresel tutum
KW  - özellik seçimi
KW  - makine öğrenmesi
KW  - sürdürülebilirlik
N2  - This study aims to develop a data-driven model to predict individuals' environmental attitudes based on machine learning (ML) algorithms. The study compares the performance of five different ML algorithms—Support Vector Machines, Gradient Boosting (GB), Multilayer Perceptron, Quadratic Discriminant Analysis, and Bagging—in classifying environmental awareness levels. During the model development process, statistical methods such as one-way analysis of variance (ANOVA) and the Chi-Square Independence Test were applied to assess the impact of variables on classification accuracy.Experimental results indicate that ANOVA- and Chi-Square-based feature selection processes effectively enhance model performance. In particular, the GB algorithm outperforms others in terms of accuracy, precision, and F1 score. The findings demonstrate that ML algorithms provide a robust analytical framework for modelling and predicting environmental attitudes. This study highlights the significance of evaluating environmental awareness levels through data-driven approaches, contributing to the development of sustainable environmental policies and the enhancement of individuals' environmental consciousness.
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UR  - https://dergipark.org.tr/tr/pub/emobd/issue//1652505
L1  - https://dergipark.org.tr/tr/download/article-file/4666507
ER  -