Adaptive Weighting with SMOTE for Learning from Imbalanced Datasets: A Case Study for Traffic Offence Prediction

机译：从非衡度数据集学习的自适应加权：交通违法预测的案例研究

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This paper proposes to augment the prediction capability of a classifier or of an ensemble of classifiers for an imbalanced set using a combination of informed sampling based on SMOTE (Synthetic Minority Oversampling Technique) and a post-classification adaptive weighting that takes into account a priori knowledge about a dataset. As a case study, the paper analyzes the relationship between traffic tickets (provincial offence notices), their types and the trends in attributes such as vehicle type, offence type, location, ticket status for the city of Ottawa, Canada with the purpose of enabling a proactive traffic enforcement.

机译：本文建议使用基于SMOTE（合成少数竞争性超法技术）的通知采样的组合和考虑先验知识的分类后自适应加权来增加分类器或分类器的分类器的集装作用的预测能力。关于数据集。作为一个案例研究，分析了交通票（省犯罪通知）之间的关系，他们的类型和诸如车辆类型，冒犯类型，地点，加拿大市渥太华市的票据地位的属性的关系，目的是实现一个积极的交通执法。

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《IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications》|2018年|162p|共6页
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Naga Prasanthi Bobbili; Ana-Maria Cretu;
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正文语种
中图分类计算机软件;
关键词
Decision trees; Market research; Machine learning; Accidents; Suspensions; Urban areas; Vehicles;

机译：决策树;市场研究;机器学习;事故;悬浮液;城市地区;车辆;

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1. Learning imbalanced datasets based on SMOTE and Gaussian distribution [J] . Information Sciences: An International Journal . 2020,第期

机译：基于SMOTE和高斯分布学习不平衡数据集
2. SSOMaj-SMOTE-SSOMin: Three-step intelligent pruning of majority and minority samples for learning from imbalanced datasets [J] . Susan Seba, Kumar Amitesh Applied Soft Computing . 2019,第期

机译：Ssomaj-Smote-SSOMIN：大多数和少数民族样本的三步智能修剪，用于从不平衡的数据集学习
3. DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets [J] . Zhang Zhong-Liang, Luo Xing-Gang, Gonzalez Sergio, Neurocomputing . 2018,第APRa12期

机译：DRCW-ASEG：针对多类不平衡数据集的基于一对一距离的相对能力加权与自适应合成示例生成
4. Adaptive Weighting with SMOTE for Learning from Imbalanced Datasets: A Case Study for Traffic Offence Prediction [C] . Naga Prasanthi Bobbili, Ana-Maria Cretu IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications . 2018

机译：利用SMOTE进行自适应加权以从不平衡数据集中学习：交通犯罪预测的案例研究
5. Active learning with support vector machines for imbalanced datasets and a method for stopping active learning based on stabilizing predictions. [D] . Bloodgood, Michael. 2009

机译：支持向量机用于不平衡数据集的主动学习，以及一种基于稳定预测的主动学习停止方法。
6. Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection [O] . Jae-Hyun Seo, Yong-Hyuk Kim 2018

机译：机器学习方法用于在类别不平衡数据集中优化SMOTE比率以进行入侵检测
7. SAMPLING BASED APPROACHES TO HANDLE IMBALANCES IN NETWORK TRAFFIC DATASET FOR MACHINE LEARNING TECHNIQUES [O] . Raman Singh, Harish Kumar, R. K. Singla 2014

机译：基于采样的机器学习技术在网络流量数据集中处理不平衡的方法

Adaptive Weighting with SMOTE for Learning from Imbalanced Datasets: A Case Study for Traffic Offence Prediction

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