.png){kind=icon}
返回
Graph based ensemble classification for crime report prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2022.109215.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Criminology and crime analysis are growing areas of research dealing with huge amount of past crime reports. Classification analysis identifies the crime patterns among the reports and predicts the crime category of new reports. As different classifiers classify a data set with different accuracy, so ensemble classification is the appropriate choice for many applications. The present work demonstrates a graph based ensemble classification approach to predict crime reports more accurately than the individual classifiers. Initially, the crime reports are graphically modelled to find a set of maximal independent subset of features and subsequently, decision tree based classifiers are designed using these subsets. Next, a graph is constructed with generated decision trees as nodes and the same method is applied to find out a set of maximal independent subsets of decision trees, each of which is considered as an initial ensemble classifier. Finally, these classifiers are ensemble using the traditional Boolean algebra properties to generate the final ensemble classifier. The developed model may be helpful for crime analysis by the law enforcement agencies for controlling the criminal activities in the country. Extensive experiments are conducted for evaluating the performance of the proposed method on several crime data sets. The superior performance demonstrates the effectiveness of the developed ensemble classification model and indicates its wide potential applications in crime domain. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Natural language processing
Decision tree
Maximal independent set
Ensemble classification
Crime report prediction
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach使用堆叠泛化的机器学习算法进行犯罪预测的实证分析: 集成方法.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Crimmigration checks in the internal border areas of the EU: Finding the discretion that mattersOAAI
OAAI分享收藏
查看更多内容