A Comparative Study to Evaluate Filtering Methods for Crime Data Feature Selection

Mohd, Fatihah; Noor, Noor Maizura Mohamad

doi:10.1016/j.procs.2017.10.018

Cited by 15 publications

(2 citation statements)

References 12 publications

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“…Based on the foregoing, it would be most correct to consider "combating crime" as a term that unites all the activities. Prevention should become relevant when it comes to a potential but not yet conceived crime (prevention) [7][8][9]; about a crime conceived and prepared but not yet committed at the stage of preparation (prevention) [10,11]; about a crime started but still unfinished at the stage of attempt (suppression) [12]. If a crime is over, then it is time to carry out measures to combat (identification, disclosure, investigation, etc.)…”

Section: Resultsmentioning

confidence: 99%

Prevention of crime by criminal law and operational-search means

et al. 2021

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In the early 2000s, the Russian legislator massively introduced the term “preventing crime” into regulations thus replacing the concept of “fighting against crime”. Thus, the changes influenced federal law No. 130-FZ “Combating Terrorism” dated 25 July 1998 and many other laws. The very concept of the state’s response to violation of the established prohibitions has changed. If in the old version of the laws, punishment for committing a crime was the main preventive measure, then in the new understanding the key efforts of the state should have been focused on preventing the very event of a crime. On the one hand, this is an absolutely correct step, since it is much more profitable for the state (in socio-economic, political and other respects) to keep the population from violating the established rules than to be forced to launch a complex and expensive criminal procedural mechanism (to identify, disclose, investigate crimes, consider them in court, execute punishment, etc.). On the other hand, in the new laws, the term “prevention” is used ambiguously, to both characterise “crime prevention” activities and characterise “crime control” activities. The research objective is to find the most optimal ways to eliminate theoretical and practical contradictions arising from the law enforcement in connection with the tautology of the texts of federal laws in the field of combating crime. In the course of the research, the dialectical method of cognition was used, as well as general scientific (analysis and synthesis, induction and deduction, logical, systemic and structural methods) and specific scientific methods of cognition (historical, statistical and formal-legal). It is proposed to unify the definition of “combating crime” by introducing the same definitions into the federal law “Operational Investigative Activity”, “Countering Terrorism”, and other regulatory documents related to “combating crime”.

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Section: Resultsmentioning

confidence: 99%

Prevention of crime by criminal law and operational-search means

et al. 2021

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“…Governments and police have access to a large amount of data that could be used to help reduce the crime rate (Yuki et al 2019). Crime pattern theory also suggests that offenders prefer not to venture into unknown territories or areas and that they would rather commit opportunistic and violent crimes by taking advantage of familiar areas they have previously encountered (Jalil et al 2017). Crimes are also not distributed evenly and uniformly nor are they random in an area or city.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in crime prediction

Jenga

Çatal

Kar

2023

J Ambient Intell Human Comput

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Predicting crimes before they occur can save lives and losses of property. With the help of machine learning, many researchers have studied predicting crimes extensively. In this paper, we evaluate state-of-the-art crime prediction techniques that are available in the last decade, discuss possible challenges, and provide a discussion about the future work that could be conducted in the field of crime prediction. Although many works aim to predict crimes, the datasets they used and methods that are applied are numerous. Using a Systematic Literature Review (SLR) methodology, we aim to collect and synthesize the required knowledge regarding machine learning-based crime prediction and help both law enforcement authorities and scientists to mitigate and prevent future crime occurrences. We focus primarily on 68 selected machine learning papers that predict crime. We formulate eight research questions and observe that the majority of the papers used a supervised machine learning approach, assuming that there is prior labeled data, and however in some cases, there is no labeled data in real-world scenarios. We have also discussed the main challenges found while conducting some of the studies by the researchers. We consider that this research paves the way for further research to help governments and countries fight crime and decrease this for better safety and security.

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