Parameter optimization of gradient tree boosting using dragonfly algorithm in crime forecasting and analysis

• Main Authors: Khairuddin, A. R., Ali, N. A., Alwee, R., Haron, H., Zain, A. M.
• Format: Article
• Language: English
• Published: Science Publications 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/89910/1/AlifRidzuanKhairuddin2019_ParameterOptimizationofGradientTree.pdf; http://eprints.utm.my/id/eprint/89910/; https://dx.doi.org/10.3844/jcssp.2019.1085.1096

Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB's overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB's three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to nonoptimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.