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Browsing by Author "Kumbasar, M."

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    Predicting Electric Vehicle Adoption in the Eu: Analyzing Classification Performance and Influencing Attributes Across Countries, Gender, and Education Level
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kumbasar, M.; Tokdemir, G.; Labben, T.G.; Ertek, G.; Bilgisayar Mühendisliği
    Electric vehicles (EVs) have been one of the trending technologies in recent decades, as they are expected to transform the current automotive technology and transportation systems. To this end, the scope of this study is analyzing survey data on European consumers' EV purchase decisions. The objective is comparing the predictive quality of various classification algorithms in predicting EV adoption, across country, gender and education level of the participants, as well as the analysis of the influencing attributes. Initially, the data is filtered for each value of the chosen categorical attribute (country, gender or education level) with the missing values being imputed. Then, several classification algorithms in the Python sklearn package are applied through 5-fold-cross validation and the performance of the algorithms are compared based on standard classification metrics. There are notable variations in classification performance and influencing attributes depending on the values of the selected categorical attributes. © 2024 IEEE.
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    Topic-Aware Multi-Class Classification for Financial Complaints: Comparing BERTopic With Classical Machine Learning Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2025) Uǧuz, S.; Kumbasar, M.; Tokdemir, G.
    In today's digital world, customers can utilize a variety of communication channels, such as business emails, consumer forms, feedback platforms, and dedicated complaint websites, to communicate their complaints. This study compares the performance of the supervised Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic) with traditional machine learning algorithms, including Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), for multi-class classification of financial customer complaints. The dataset consists of 16,715 balanced training data and 3,808 test data across five different categories, with the financial complaint data. Experimental results demonstrate that traditional machine learning models, particularly XGBoost, SVM, and LR, achieved the highest classification performance with accuracy rates close to 88%. BERTopic showed a competitive performance with an accuracy of 82.48%. The results suggest that while BERTopic offers interpretability advantages through topic modeling techniques, traditional algorithms provide higher accuracy. This study highlights the promising potential for future financial text analysis and customer complaint classification using hybrid methods, which could lead to more detailed, topic-aware classification approaches. © 2025 IEEE.