KLCBL: An Improved Police Incident Classification Model

• URL: http://arxiv.org/abs/2411.06749v1
• Date: Mon, 11 Nov 2024 07:02:23 GMT
• Title: KLCBL: An Improved Police Incident Classification Model
• Authors: Liu Zhuoxian, Shi Tuo, Hu Xiaofeng,
• Abstract summary: Police incident data is crucial for public security intelligence, yet grassroots agencies struggle with efficient classification due to manual inefficiency and automated system limitations. 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. The model addresses classification challenges, enhances police informatization, improves resource allocation, and offers broad applicability to other classification tasks.
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• Abstract: 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.

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