Characterising the Personality of the Public Safety Offender and Non‐offender using Decision Trees: The Case of Colombia

Masías, Víctor Hugo; Valle, Mauricio A.; Amar, José Juan Amar; Cervantes, Marco; Brunal, Gustavo; Crespo, Fernando

doi:10.1002/jip.1451

Cited by 4 publications

(5 citation statements)

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“…Then, the tree was pruned, using a tenfold cross validation procedure to determine the optimal tree size. In this way, the tree whose complexity parameter minimized the mean error of the cross-validation ( x error ) was selected [16,50].…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, although traditional statistical methods focusing on mere descriptions of the phenomena continue to be used in investigations, the use of more sophisticated statistical analysis methods such as multivariate techniques has become more popular. One of the advantages of these statistical procedures is that they consider the combined relationships between all elements that are linked to homicides, thereby ensuring a better understanding of the phenomena and permitting useful conclusions to be reached [6,15,16,17]. In this way, studies focusing on prediction stand out, specifically those focusing on the applied nature of criminal profiling during police investigations, which has been conceived as a supplementary technique to help in the identification and arrest of criminals [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

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Homicide Profiles Based on Crime Scene and Victim Characteristics

Pecino-Latorre

Fuentes

Patró-Hernández

2019

IJERPH

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One of the current trends in homicide research includes developing works based on scientific study and empirical evidence, which offer conclusions that can be used in an operational manner during police investigations. The objective of this study was to identify homicide characteristics from behaviors carried out on the crime scene and victim characteristics associated with those of the perpetrators of these crimes in Spain. The sample consisted of 448 homicide cases from the database of the Homicide Revision Project led by the Office of Coordination and Studies of the Secretary of State and Security. After creating six classification tree models, it was found that the modus operandi of the aggressor and the victim characteristics may permit hypothesizing about the demographic characteristics of the perpetrator (gender, age, and country of origin), his/her criminal record, and the type of relationship with the victim. Furthermore, the importance of the study of victimology during a criminal investigation is highlighted, as it may indirectly offer information about the potential perpetrator. The findings of this study suggest that criminal profiling contributes notably to the decision-making process to establish more rigorous suspect prioritization, improve the management of human resources and materials, and increase the efficiency of criminal investigations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Homicide Profiles Based on Crime Scene and Victim Characteristics

Pecino-Latorre

Fuentes

Patró-Hernández

2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…It is noticeable that knowledge acquisition is the most common processes used to enhance AI systems (N = 6), followed by both knowledge creation and knowledge application/decision-making process (N = 6), knowledge sharing (N = 2), and Knowledge discovery with the least number of studies (N = 1). Referring to Table 4, it seems that knowledge application and decision-making processes have a positive impact on Neural Networks, including ANN, RNN, and CNN [31], Decision Trees [33], and Chatbots [56]. On the other hand, [52] argued that knowledge management application and decision making could have some negative impact on Neural Networks, including ANN, RNN, and CNN, in some applications.…”

Section: Resultsmentioning

confidence: 99%

“…Artificial neural networks and data mining work in parallel for the CRM system in order to generate knowledge about customers' behaviors and to predict their willingness to purchase specific items [32]. Additionally, in [33] study, they express the importance of using knowledge management in order to feed the AI system as it is essential for personal characterization, which used to improve public safety using the decision tree algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

The Role of KM in Enhancing AI Algorithms and Systems

AlGhanem¹,

Shanaa²,

Salloum³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Knowledge Management processes present a vital role in improving AI systems and algorithms. Many studies and reviews were carried out to examine the relationship between KM processes and AI systems. However, studies were focusing on specific methods and the impact on some AI algorithms, neglecting the role of other KM processes and how it may affect the AI system to achieve the objective, which reduces the adoption in some organizations. The current study shows the relation between KM processes and AI systems from a higher perspective, giving different options to apply other KM processes for the same AI algorithm to reduce any implementation challenges and enhance the adoption level. The review looks into 16 studies collected from a different database from 2014 to 2019. The main finding of the research was the massive impact of some KM processes like knowledge acquisition and knowledge creation on the different types of AI systems and algorithms to give an additional option for organizations during the implementation. Additionally, the research finds that most of the studies agree on the positive relationship between knowledge management processes and the role-plays to enhance AI systems and algorithms. Finally, the study shows a decrease in the number of researches done for this topic in the selected databases, which can be enhanced by other researchers by examining other databases to increase results accuracy.

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“…Valuable though these three analyses are, they all confine their methodologies to the use of Logistic Regression as a data classifier. Studies in other contexts comparing different classifiers have shown that that their performance can vary significantly depending on the data domain they are applied to [ 29 – 35 ]. This suggests that classification techniques other than Logistic Regression should be evaluated to determine how well they perform comparatively with criminal network data.…”

Section: Introductionmentioning

confidence: 99%

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

et al. 2016

Self Cite

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Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

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Characterising the Personality of the Public Safety Offender and Non‐offender using Decision Trees: The Case of Colombia

Cited by 4 publications

References 95 publications

Homicide Profiles Based on Crime Scene and Victim Characteristics

Homicide Profiles Based on Crime Scene and Victim Characteristics

The Role of KM in Enhancing AI Algorithms and Systems

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

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