Abstract
In this study, we propose a hybrid crime classification model by combining artificial neural network (ANN), particle swarm optimization (PSO) and grey relation analysis (GRA). The objective of this study is to identify the significant features of the specific crimes and to classify the crimes into three different categories. The PSO as the feature selection method, reduce the dimension of datasets by selecting the most significant features. The reduction of the datasets’ dimension may reduce the complexity thus shorten the running time of ANN to classify the crime datasets. The GRA is used to rank the selected features of the specific crimes thus visualize the importance of the selected crime’s attribute. The experiment is carried out on the Communities and Crime dataset. The result of PSO feature selection will then compare with the other feature selection methods such as evolutionary algorithm (EA) and genetic algorithm (GA). The classification performance for each feature selection method will be evaluated. From our experiments, we found that PSO select less features compare with EA and GA. The classification performance results show that the combination of PSO with ANN produce less error and shorten the running time compare with the combination of EA with ANN and GA with ANN.
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Abu Hana, R.O., Freitas, C.O., Oliveira, L.S., Bortolozzi, F.: Crime scene classification. In: Proceedings of the 2008 ACM Symposium on Applied computing, pp. 419–423. ACM Press (2008)
Adderley, R.: The use of data mining techniques in operational crime fighting. In: Chen, H., Moore, R., Zeng, D.D., Leavitt, J. (eds.) ISI 2004. LNCS, vol. 3073, pp. 418–425. Springer, Heidelberg (2004)
Alwee, R., Shamsuddin, S.M.H., Sallehuddin, R.S.: Economic indicators selection for crime rates forecasting using cooperative feature selection. In: Proceeding of the 20th National Symposium on Mathematical Sciences Research in Mathematical Sciences: A Catalyst for Creativity and Innovation, vol. 1522, pp. 1221–1231. AIP Publishing (2013)
Anuar, M.S., Selamat, A., Sallehuddin, R.: Particle swarm optimization feature selection for violent crime classification. In: Advanced Approaches to Intelligent Information and Database Systems, pp. 97–105. Springer (2014)
Bache, K., Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
Chang, T.C., Lin, S.J.: Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in taiwan. Journal of Environmental Management 56(4), 247–257 (1999)
Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime data mining: a general framework and some examples. Computer 37(4), 50–56 (2004)
Deng, J.-L.: Introduction to grey system theory. The Journal of Grey System 1(1), 1–24 (1989)
Gorr, W., Olligschlaeger, A., Thompson, Y.: Assessment of crime forecasting accuracy for deployment of police. International Journal of Forecasting (2000)
Iqbal, R., Murad, M.A.A., Mustapha, A., Panahy, S., Hassany, P., Khanahmadliravi, N.: An experimental study of classification algorithms for crime prediction. Indian Journal of Science & Technology 6(3) (2013)
Theresa, M.J., Raj, V.J.: Fuzzy based genetic neural networks for the classification of murder cases using trapezoidal and lagrange interpolation membership functions. Applied Soft Computing 13(1), 743–754 (2013)
Kamiran, F., Karim, A., Verwer, S., Goudriaan, H.: Classifying socially sensitive data without discrimination: an analysis of a crime suspect dataset. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 370–377. IEEE (2012)
Kim, J.-M., Ahn, H.-K., Lee, D.-H.: A study on the occurrence of crimes due to climate changes using decision tree. In: IT Convergence and Security 2012, pp. 1027–1036. Springer (2013)
Kung, C.-Y., Yan, T.-M., Chuang, S.-C., Wang, J.-R.: Applying grey relational analysis to assess the relationship among service quality customer satisfaction and customer loyalty. In: 2006 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–5. IEEE (2006)
Lu, J., Chen, P., Shen, J., Liang, Z., Yang, H.: Study on the prediction of gas content based on grey relational analysis and bp neural network. In: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013, pp. 677–685. Springer (2013)
Nasridinov, A., Ihm, S.-Y., Park, Y.-H.: A decision tree-based classification model for crime prediction. In: Information Technology Convergence, pp. 531–538. Springer (2013)
Nath, S.V.: Web Intelligence and Intelligent Agent Technology Workshops. In: 2006 IEEE/WIC/ACM International Conference on. WI-IAT 2006 Workshops, pp. 41–44. IEEE (2006)
Nissan, E.: An overview of data mining for combating crime. Applied Artificial Intelligence 26(8), 760–786 (2012)
Omar, N., Shahizan, M., Othman, o.b.: Particle swarm optimization feature selection for classification of survival analysis in cancer. International Journal of Innovative Computing 2(1) (2013)
Ozgul, F., Atzenbeck, C., Çelik, A., Erdem, Z.: Incorporating data sources and methodologies for crime data mining. In: 2011 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 176–180. IEEE (2011)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)
Yang, R., Olafsson, S.: Classification for predicting offender affiliation with murder victims. Expert Systems with Applications 38(11), 13518–13526 (2011)
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Anuar, S., Selamat, A., Sallehuddin, R. (2015). Hybrid Particle Swarm Optimization Feature Selection for Crime Classification. In: Barbucha, D., Nguyen, N., Batubara, J. (eds) New Trends in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-16211-9_11
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DOI: https://doi.org/10.1007/978-3-319-16211-9_11
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