使用集成合成器（ENSY）改进出行方式选择建模
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)
AI总结：
本文提出利用概率分布进行数据增强的 Ensemble Synthesizer（ENSY），以改善出行方式选择数据集中少数类识别难的问题；实验表明其显著提升少数类 F1 与整体准确率，并优于多种基线增强方法。
摘要：
对出行方式选择数据集进行准确分类，对于交通规划和决策过程至关重要。然而，传统分类模型往往难以充分捕捉这些数据集中少数类的细微模式，从而导致准确率欠佳。为应对这一挑战，我们提出了 Ensemble Synthesizer（ENSY），它利用概率分布进行数据增强，是一种专门为提升出行方式选择数据集分类准确率而设计的新型数据模型。在我们的研究中，ENSY 展现出显著效果：少数类的 F1 分数几乎提高至原来的四倍，整体分类准确率提高近 3%。为全面评估其性能，我们将 ENSY 与包括 Random Oversampling、SMOTE-NC 和 CTGAN 在内的多种增强技术进行比较。通过实验，ENSY 在各种场景下始终优于这些方法，凸显了其稳健性和有效性。
英文摘要：
Accurate classification of mode choice datasets is crucial for transportation planning and decision-making processes. However, conventional classification models often struggle to adequately capture the nuanced patterns of minority classes within these datasets, leading to sub-optimal accuracy. In response to this challenge, we present Ensemble Synthesizer (ENSY) which leverages probability distribution for data augmentation, a novel data model tailored specifically for enhancing classification accuracy in mode choice datasets. In our study, ENSY demonstrates remarkable efficacy by nearly quadrupling the F1 score of minority classes and improving overall classification accuracy by nearly 3%. To assess its performance comprehensively, we compare ENSY against various augmentation techniques including Random Oversampling, SMOTE-NC, and CTGAN. Through experimentation, ENSY consistently outperforms these methods across various scenarios, underscoring its robustness and effectiveness
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
