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强化学习驱动的特征选择,通过一种进化方法来增强,针对犯罪嫌疑人的识别进行调整

Zhenming Gao¹, Zhang Jian², Seyed Jalaleddin Mousavirad³

¹School of Management, University of Sheffield, Sheffield, UK.

Scientific reports

|November 25, 2025

概括

本研究介绍了一种先进的方法,使用强化学习 (RL) 和差异演化 (DE) 来准确识别犯罪嫌疑人. 这种新的方法有效地处理特征选择和类不平衡,优于传统技术.

科学领域:

计算机科学计算机科学
人工智能的人工智能
法医科学法医科学法医科学

背景情况:

准确识别犯罪嫌疑人对于正义和预防犯罪至关重要.
卷积神经网络 (CNN) 是常见的,但在特征选择,类失衡和超参数调整方面面临挑战.
这些局限性降低了传统的基于CNN的嫌疑人识别方法的有效性.

研究的目的:

开发一种新的犯罪嫌疑人识别策略,克服传统方法的局限性.
通过整合强化学习 (RL) 和差异进化 (DE) 算法来提高CNN的有效性.
改进特征选择,解决类不平衡,优化超参数在嫌疑人检测.

主要方法:

实施了非政策近接政策优化 (Off-policy PPO) 算法用于强化学习,以解决特征选择和类失衡问题.
使用差异进化 (DE) 算法,增强了对突变策略的k-平均集群,以调整超参数.
雇佣了政策之外的PPO,因为其减少了数据要求,并在昂贵的数据收集设置中提高了效率.

主要成果:

拟议的方法在四个数据集中实现了高F测量:CelebA (89.409%),LFW (91.152%),CASIA-WebFace (92.184%) 和VGGFace2 (92.202%).
在犯罪嫌疑人检测方面,Off-policy PPO动态调整的方法始终超越了传统的静态方法.
增强的DE算法通过其新型突变战略有效地识别了关键集群.

结论:

开发的方法显著优于现有的犯罪嫌疑人识别方法.
这项研究提升了早期嫌疑人检测能力,并增强了调查策略.
整合RL和DE为具有有限或不平衡数据的复杂识别任务提供了强大的解决方案.

关键词:

卷积神经网络是一种卷积神经网络.犯罪犯罪犯罪犯罪犯罪嫌疑人犯罪嫌疑人不同进化的差异进化.邻近政策优化政策优化

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