Abstract
Nowadays, the Mexican government is showing a great interest in decreasing the crime rate in Mexico. A way to carry out this task is to understand criminal behavior in each Mexico states by using an eXplainable Artificial Intelligence (XAI) model. In this paper, we propose to understand the criminal behavior of the Mexico city by using an XAI model jointly with our proposed feature representation based on the weather. Our experimental results show how our proposed feature representation allows for improving all tested classifiers. Also, we show that the XAI-based classifier improves other tested state-of-the-art classifiers.
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- 1.
Homicide rate is computed per year per 100,000 inhabitants.
- 2.
A pattern is considered a pure pattern when it covers objects for only one class.
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Author is thankful to Prof. Miguel Angel Medina-Pérez, PhD; for his valuable contributions improving the grammar and style of this paper.
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Loyola-González, O. (2019). Understanding the Criminal Behavior in Mexico City through an Explainable Artificial Intelligence Model. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_12
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