KLCBL模型提升警情分类准确率至91.9%，助力公安智能化
KLCBL: An Improved Police Incident Classification Model
- 融合KAN、LERT、CNN与BiLSTM的多通道神经网络架构
- 在真实数据上达到91.9%分类准确率，优于基线模型
- 适用于电信诈骗等复杂警情分类，推动警务信息化
警情数据对公共安全智能研判至关重要，但基层单位因人工效率低和自动化系统局限，在电信及网络诈骗类案件的分类上面临挑战。本文提出一种多通道神经网络模型KLCBL，结合柯尔莫戈洛夫-阿诺德网络（KAN）、语言增强文本预处理方法（LERT）、卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM），用于警情分类。基于真实数据评估，KLCBL模型实现91.9%的分类准确率，显著优于基线模型。该模型有效应对分类难题，提升公安信息化水平，优化资源配置，并可广泛应用于其他分类任务。
原文摘要 · Abstract (English)
Police incident data is crucial for public security intelligence, yet grassroots agencies struggle with efficient classification due to manual inefficiency and automated system limitations, especially in telecom and online fraud cases. This research proposes a multichannel neural network model, KLCBL, integrating Kolmogorov-Arnold Networks (KAN), a linguistically enhanced text preprocessing approach (LERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) for police incident classification. Evaluated with real data, KLCBL achieved 91.9% accuracy, outperforming baseline models. The model addresses classification challenges, enhances police informatization, improves resource allocation, and offers broad applicability to other classification tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。