用孪生网络提升犯罪关联分析准确率，最高提升9%。
Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network
- 设计孪生自编码框架，融合时空特征增强行为表示
- 在真实犯罪数据上使AUC最高提升9%，性能更稳定
- 适合刑侦分析、犯罪预测等司法人工智能应用
有效的犯罪关联分析对识别连环罪犯、提升公共安全至关重要。针对传统方法在处理高维、稀疏、异构数据时的局限性，本文提出一种孪生自编码框架，通过学习有意义的潜在表示并揭示复杂犯罪数据中的关联。基于英国国家犯罪局严重犯罪分析部门维护的暴力犯罪关联分析系统（ViCLAS）数据，该方法在解码器阶段整合地理-时间特征，缓解稀疏特征空间中的信号稀释问题，强化行为表示而非在输入层被掩盖。实验显示，该方法在多个评估指标上均实现一致提升。进一步分析不同领域先验的数据降维策略对模型性能的影响，为犯罪关联场景的预处理提供实践指导。结果表明，先进机器学习方法显著提升关联准确性，相较传统方法最高提升AUC达9%，同时提供可解释洞察以支持调查决策。
原文摘要 · Abstract (English)
Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address limitations of traditional crime linkage methods in handling high-dimensional, sparse, and heterogeneous data, we propose a Siamese Autoencoder framework that learns meaningful latent representations and uncovers correlations in complex crime data. Using data from the Violent Crime Linkage Analysis System (ViCLAS), maintained by the Serious Crime Analysis Section of the UK's National Crime Agency, our approach mitigates signal dilution in sparse feature spaces by integrating geographic-temporal features at the decoder stage. This design amplifies behavioral representations rather than allowing them to be overshadowed at the input level, yielding consistent improvements across multiple evaluation metrics. We further analyze how different domain-informed data reduction strategies influence model performance, providing practical guidance for preprocessing in crime linkage contexts. Our results show that advanced machine learning approaches can substantially enhance linkage accuracy, improving AUC by up to 9% over traditional methods while offering interpretable insights to support investigative decision-making.
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