Combination of Support Vector Machine (SVM) and Bayesian Model to Identify Criminal Language

Viloria, Amelec; Lezama, Omar Bonerge Píneda; Hurtado, Juan Carlos Tójar

doi:10.1007/978-981-15-8443-5_20

Cited by 1 publication

(1 citation statement)

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“…Among them, we chose Naive Bayes, for its predictive effectiveness, despite its simplicity, both reasons for its widespread use to model complex real-world situations in many different areas, such as biomedical data [13], medicine [14] or robotics [15], among others. In criminology, Naive Bayes has been used for crime prediction which involves finding the most likely offender for a particular crime incident when the incident history is provided with the incident-level crime data in [16], and also for crimes related to social media such as Cyber Stalking, Cyber Harassment, Cyber Hacking and Cyber Scam in [17], and has been combined with a Support Vector Machine to identify criminal language for automatic text classification in [18], just to mention some recent works.…”

Section: Goals and Toolsmentioning

confidence: 99%

The effect of seasonality in predicting the level of crime. A spatial perspective

Delgado,

Sánchez-Delgado

2023

PLoS ONE

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This paper presents an innovative methodology to study the application of seasonality (the existence of cyclical patterns) to help predict the level of crime. This methodology combines the simplicity of entropy-based metrics that describe temporal patterns of a phenomenon, on the one hand, and the predictive power of machine learning on the other. First, the classical Colwell’s metrics Predictability and Contingency are used to measure different aspects of seasonality in a geographical unit. Second, if those metrics turn out to be significantly different from zero, supervised machine learning classification algorithms are built, validated and compared, to predict the level of crime based on the time unit. The methodology is applied to a case study in Barcelona (Spain), with month as the unit of time, and municipal district as the geographical unit, the city being divided into 10 of them, from a set of property crime data covering the period 2010-2018. The results show that (a) Colwell’s metrics are significantly different from zero in all municipal districts, (b) the month of the year is a good predictor of the level of crime, and (c) Naive Bayes is the most competitive classifier, among those who have been tested. The districts can be ordered using the Naive Bayes, based on the strength of the month as a predictor for each of them. Surprisingly, this order coincides with that obtained using Contingency. This fact is very revealing, given the apparent disconnection between entropy-based metrics and machine learning classifiers.

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