Abstract
Crime prevention is an important roles in police system for any country. Crime classification is one of the components in crime prevention. In this study, we proposed a hybrid crime classification model by combining Artificial Neural Network (ANN) and Artificial Bee Colony (ABC) algorithm (codename ANN-ABC). The idea is by using ABC as a learning mechanism for ANN to overcome the ANN’s local optima problem thus produce more significant results. The ANN-ABC is applied to Communities and Crime dataset to predict ’Crime Categories’. The dataset was collected from UCI machine learning repository. The result of ANN-ABC will be compare with other classification algorithms. The experiment results show that ANN-ABC outperform other algorithms and achieved 86.48% accuracy with average 7% improvement compare to other algorithms.
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Anuar, S., Selamat, A., Sallehuddin, R. (2015). Hybrid Artificial Neural Network with Artificial Bee Colony Algorithm for Crime Classification. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-13153-5_4
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DOI: https://doi.org/10.1007/978-3-319-13153-5_4
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