Abstract
Gradient tree boosting (GTB) is a newly emerging artificial intelligence technique in crime forecasting. GTB is a stage-wise additive framework that adopts numerical optimisation methods to minimise the loss function of the predictive model which later enhances it predictive capabilities. The applied loss function plays critical roles that determine GTB predictive capabilities and performance. GTB uses the least square function as its standard loss function. Motivated by this limitation, the study is conducted to observe and identify a potential replacement for the current loss function in GTB by applying a different existing standard mathematical function. In this study, the crime models are developed based on GTB with a different loss function to compare its forecasting performance. From this case study, it is found that among the tested loss functions, the least absolute deviation function outperforms other loss functions including the GTB standard least square loss function in all developed crime models.
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This work was supported by FRGS research grant granted by Malaysian Ministry of Education with grant number R.J130000.7828.4F825 for School of Computing, Universiti Teknologi Malaysia (UTM).
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Khairuddin, A.R., Alwee, R., Haron, H. (2020). A Proposed Gradient Tree Boosting with Different Loss Function in Crime Forecasting and Analysis. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_18
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DOI: https://doi.org/10.1007/978-3-030-33582-3_18
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