基于梯度树增强的混合邻域成分分析在犯罪率预测中的特征选择

Q4 Computer Science
A. R. Khairuddin, R. Alwee, H. Haron
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引用次数: 0

摘要

犯罪预测是有益的,因为它为政府和当局提供了宝贵的信息,以规划有效的预防犯罪措施。大多数犯罪学研究发现,社会、人口和经济等因素的影响对犯罪的发生有显著影响。因此,大多数犯罪学专家和研究人员研究和观察因素对犯罪活动的影响,因为它提供了对未来可能的犯罪趋势的相关见解。基于文献综述,适当的分析在识别影响犯罪的重要因素方面的应用是稀缺和有限的。因此,本研究提出了一个混合模型,将邻里成分分析(NCA)与梯度树增强(GTB)相结合,对美国(US)犯罪率数据进行建模。NCA是一种特征选择技术,在本研究中用于识别影响犯罪率的显著因素。一旦确定了重要因素,就采用人工智能技术,即GTB,对犯罪数据进行建模,从而预测犯罪率值。通过定量测量误差分析,将所提模型的性能与其他已有模型进行了比较。结果表明,NCA-GTB模型在预测犯罪率方面优于其他犯罪模型。实验结果表明,该模型在实例研究中产生的定量测量误差最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Neighbourhood Component Analysis with Gradient Tree Boosting for Feature Selection in Forecasting Crime Rate
Crime forecasting is beneficial as it provides valuable information to the government and authorities in planning an efficient crimeprevention measure. Most criminology studies found that influence from several factors, such as social, demographic, and economicfactors, significantly affects crime occurrence. Therefore, most criminology experts and researchers study and observe the effectof factors on criminal activities as it provides relevant insight into possible future crime trends. Based on the literature review, theapplications of proper analysis in identifying significant factors that influence crime are scarce and limited. Therefore, this study proposed a hybrid model that integrates Neighbourhood Component Analysis (NCA) with Gradient Tree Boosting (GTB) in modelling the United States (US) crime rate data. NCA is a feature selection technique used in this study to identify the significant factors influencing crime rate. Once the significant factors were identified, an artificial intelligence technique, i.e., GTB, was implemented in modelling the crime data, where the crime rate value was predicted. The performance of the proposed model was compared with other existing models using quantitative measurement error analysis. Based on the result, the proposed NCA-GTB model outperformed other crime models in predicting the crime rate. As proven by the experimental result, the proposed model produced the smallest quantitative measurement error in the case study.
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
95
期刊介绍: IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area. Topics covered include: -Information theory/coding- Information/IT/network security, standards, applications- Internet/web based systems/products- Data mining/warehousing- Network planning, design, administration- Sensor/ad hoc networks- Human-computer intelligent interaction, AI- Computational linguistics, digital speech- Distributed/cooperative media- Interactive communication media/content- Social interaction, mobile communications- Signal representation/processing, image processing- Virtual reality, cyber law, e-governance- Microprocessor interfacing, hardware design- Control of industrial processes, ERP/CRM/SCM
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