Communications - Scientific Letters of the University of Zilina 2022, 24(3):D105-D115 | DOI: 10.26552/com.C.2022.3.D105-D115

Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model

Wuttikrai Chaipanha ORCID..., Patiphan Kaewwichian ORCID...*
Department of Civil Engineering, Faculty of Engineering, Rajamangala University of Technology Isan, Khon Kaen, Thailand

Because the numbers of cars reflect each person's travel behaviors for each specific location, the car ownership demand model plays a dominant role in analysis of the travel demand in order to understand each area's individual and household travel behaviors. However, the study project for the master plan of the Khon Kaen expressway represented imbalanced data; namely, the majority class and the minority class were not equal. Before developing a machine learning model, this study suggested a solution to balance the data by using oversampling and under-sampling techniques. The data, which had been improved with SMOTE (Synthetic Minority Oversampling Technique) and kNN (k-nearest neighbors) (k = 5), demonstrated a better effect than the other algorithms that were studied. The TPR (true positive rate) for the rural and suburban areas, which are types of regions with very different imbalance ratios, was calculated before balancing the data at 46.9 % and 46.4 %. As a result, the TPR values were 63.5 % and 54.4 %, respectively, following the data balancing.

Keywords: tour-based model, multiclass classification, k-nearest neighbors, activity-based model

Received: July 28, 2021; Accepted: January 31, 2022; Prepublished online: March 25, 2022; Published: July 1, 2022  Show citation

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Chaipanha, W., & Kaewwichian, P. (2022). Smote vs. Random Undersampling for Imbalanced Data - Car Ownership Demand Model. Communications - Scientific Letters of the University of Zilina, 24(3), D105-115. doi: 10.26552/com.C.2022.3.D105-D115
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