Adaptive Cluster-Based Synthetic Minority Oversampling Technique for Traffic Mode Choice Prediction with Imbalanced Dataset
This work addresses the problem of imbalanced datasets in urban transportation mode classification, offering an incremental improvement over existing resampling techniques.
The study tackled class imbalance in traffic mode choice prediction by proposing an adaptive cluster-based synthetic minority oversampling technique, which significantly increased F1 scores for minority classes compared to other resampling methods when used with machine learning models like random forest and extreme gradient boosting.
Urban datasets such as citizen transportation modes often contain disproportionately distributed classes, posing significant challenges to the classification of under-represented samples using data-driven models. In the literature, various resampling methods have been developed to create synthetic data for minority classes (oversampling) or remove samples from majority classes (undersampling) to alleviate class imbalance. However, oversampling approaches tend to overgeneralize minor classes that are closely clustered and neglect sparse regions which may contain crucial information. Conversely, undersampling methods potentially remove useful information on certain subgroups. Hence, a resampling approach that takes the inherent distribution of data into consideration is required to ensure appropriate synthetic data creation. This study proposes an adaptive cluster-based synthetic minority oversampling technique. Density-based spatial clustering is applied on minority classes to identify subgroups based on their input features. The classes in each of these subgroups are then oversampled according to the ratio of data points of their local cluster to the largest majority class. When used in conjunction with machine learning models such as random forest and extreme gradient boosting, this oversampling method results in significantly higher F1 scores for the minority classes compared to other resampling techniques. These improved models provide accurate classification of transportation modes.