图 书 馆
基于监督学习的空间嵌入式共同犯罪预测Spatially Embedded Co-offence Prediction Using Supervised Learning |
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| 课程网址: | http://videolectures.net/kdd2014_tayebi_cooffence_prediction/ |
| 主讲教师: | Mohammad A. Tayebi |
| 开课单位: | 西蒙·弗雷泽大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  减少犯罪和预防犯罪战略对于提高公共安全和降低社会犯罪成本至关重要。执法机构早就意识到为此目的分析共同犯罪的罪犯网络的重要性。尽管网络结构可以对犯罪预测做出重大贡献，但该领域的研究非常有限。在这里，我们通过提出使用监督学习的犯罪预测框架来解决这个重要问题。考虑到有关罪犯的可用信息，我们引入了社交、地理、地理社交和相似性特征集，用于对潜在的负面和正面的犯罪者进行分类。与其他社交网络类似，共同犯罪的网络也受到正负对分布高度倾斜的影响。为了解决阶级失衡问题，我们确定了三种犯罪合作机会，它们有助于显着降低阶级失衡率，同时保持一半的犯罪。提议的框架是在加拿大不列颠哥伦比亚省的大型犯罪数据集上进行评估的。我们对四个不同特征集的实验评估表明，新的地理社会特征是最好的预测因子。总的来说，我们通过实验证明了所提出的共同犯罪预测框架的高效性。我们相信，我们的框架不仅能让执法机构改进其减少和预防犯罪的策略，还能为犯罪者之间的犯罪联系形成提供新的犯罪学见解。 |
| 课程简介: | Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders. |
| 关 键 词: | 犯罪数据集; 监督学习; 犯罪预测框架 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 57 |