Crime has a big impact in both the human lives and the society’s growth, which needs to be addressed and controlled. Machine learning algorithms as the fanciest technology to assist decision makers in policy making has proven its reliability in showing unseen patterns in crime. This research aims to examine the capability of trees and ensemble trees in classifying crime through model development. Experiments were done to enhance the capability of the ensembles in both classification and regression. Feature extraction like synthetic minority oversampling technique was applied in order to address the problem in the imbalanced data. Different metrics relevant to classification and regression were considered in evaluating the performance of each model used. With the use of different metrics, Gradient boosted tree was found to have better classification capability in crime dataset after outperforming decision tree and random forest in both classification and regression problem. Furthermore, random forest was also found to have a promising capability in classification by regression. Therefore, it is highly recommended that this ensemble algorithm be further examined and considered in developing model in other datasets.
Crime incidents, crime report, crime patterns, Laguna, Decision Tree algorithm, KDD
Ahishakiye, E., Omulo, E. O., Taremwa, D., & Niyonzima, I. (2017). Crime Prediction Using Decision Tree (J48) Classification Algorithm. International Journal of Computer and Information Technology, 188-195.
Ajagbe, S. A., Idowu, I. R., Oladosu, J. B., & Adesina, A. O. (2020). Accuracy of Machine Learning Models for Mortality Rate Prediction in a Crime Dataset. International Journal of Information Processing and Communication, 10(1&2), 150-160.
Aldossari, B. S., Alqahtani, F., Alshahrani, N. S., Alhammam, M. M., Alzamanan, R. M., Aslam, N., & Irfanullah. (2020). A Comparative Study of Decision Tree and Naive Bayes Machine Learning Model for Crime Category Prediction in Chicago. 2020 The 6th International Conference on Computing and Data Engineering (pp. 34-38). Senya: ACM. doi:10.1145/3379247.3379279
Almaw, A., & Kadam, K. (2018). Survey Paper on Crime Prediction using Ensemble Approach. International Journal of Pure and Applied Mathematics, 118(8), 133-139. Retrieved from https://www.acadpubl.eu/jsi/2018-118-7-9/articles/8/18.pdf
Althnian, A., AlSaeed, D., Al-Baity, H., Samha, A., Dris, A. B., Alzakari, N., . . . Kurdi, H. (2021). Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain. Applied Science, 11(2), 1-18. doi:10.3390/app11020796
Banfield, R. E., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2007). A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 173-180. doi:10.1109/tpami.2007.250609.
Barnadas, M. V. (2016, September 1). Machine Learning Applied to Crime Prediction. Barcelona.
Birba, D. E. (2020). A Comparative study of data splitting algorithms for machine learning model selection. Degree Project in Computer Science and Engineering.
Ippolito, A., & Lozano, A. C. (2020). Tax Crime Prediction with Machine Learning: A Case Study in the Municipality of Sao Paulo. 22nd International Conference on Enterprise Information Systems. 1, pp. 452-459. Czech Republic: Science and Technology Publications. doi:10.5220/0009564704520459
Iqbal, R., Panahy, P. H., Murad, M. A., Mustapha, A., & Khanahmadliravi, N. (2013). An Experimental Study of Classification Algorithms for Crime Prediction. Indian Journal of Science and Technology, 6(3), 4219-4225.
Kadar, C., Iria, J., & Cvijikj, I. P. (2016). Exploring Foursquare-derived features for crime prediction in New York City. KDD – Urban Computing WS ’16. San Francisco: ACM. doi:10.1145/1235
Kim, S., Joshi, P., Kalsi, P. S., & Taheri, P. (2018). Crime Analysis Through Machine Learning. 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Vancouver: IEEE. doi:10.1109/IEMCON.2018.8614828
Lamari, Y., Freskura, B., Abdessamad, A., Eichberg, S., & Bonviller, S. d. (2020). Predicting Spatial Crime Occurrences through an Efficient Ensemble-Learning Model. International Journal of Geo-Information, 9(645), 1-20. doi:10.3390/ijgi9110645
McClendon, L., & Meghanathan, N. (2015). Using Machine Learning Algorithms to Analyze Crime Data. Machine Learning and Applications: An International Journal, 2(1), 1-12. doi:10.5121/mlaij.2015.2101
Nagpal, A. (2017, October 18). Decision Tree Ensembles- Bagging and Boosting. Retrieved from towardsdatascience: https://towardsdatascience.com/decision-tree-ensembles-bagging-and-boosting
Nguyen, T. T., Hatua, A., & Sung, A. H. (2017). Building a Learning Machine Classifier with Inadequate Data for Crime Prediction. Journal of Advances in Information Technology, 8(2), 141-147. doi:10.12720/jait.8.2.141-147
Peng, M., Zhang, Q., Xing, X., Gui, T., Huang, X., Jiang, Y.-G., . . . Chen, Z. (2019). Trainable Undersampling for Class-Imbalance Learning. The Thirty-Third AAAI Conference on Artificial Intelligence (pp. 4707-4714). Hawaii: AAAI.
Sapin, S. B., Lerios, J. L., Padallan, J. O., Buama, C. A., & Asor, J. R. (2021). Fire incidents visualization and pattern recognition using machine learning algorithms. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1427-1435. doi:10.11591/ijeecs.v22.i3
Shah, N., Bhagat, N., & Shah, M. (2021). Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention. Visual Computing for Industry, Biomedicine, and Art, 4(9), 1-14. doi:10.1186/s42492-021-00075-z
Singh, R., & Pal, S. (2020). Machine Learning Algorithms and Ensemble Technique to Improve Prediction of Students Performance. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3970-3976. doi:10.30534//ijatcse/2020/221932020
Somasundaram, A., & Reddy, U. S. (2016). Data Imbalance: Effects and Solutions for Classification of Large and Highly Imbalanced Data . 1st International Conference on Research in Engineering, Computers and Technology (pp. 28-34). Peru: IEEE.
ToppiReddy, H. K., Saini, B., & Mahajan, G. (2018). Crime Prediction & Monitoring Framework Based on Spatial Analysis. Internation Conference in Computational Intelligence and Data Science (pp. 696-705). Ohio: Elsevier. doi:10.1016/j.procs.2018.05.075
Wibowo, A. H., & Oesman, T. I. (2019). The comparative analysis on the accuracy of k-NN, Naive Bayes, and Decision Tree Algorithms in predicting crimes and criminal actions in Sleman Regency. iCAST-ES 2019. 1450, pp. 1-6. Bali: Journal of Physics: Conference Series. doi:10.1088/1742-6596/1450/1/012076
Jonard R. Asor. Instructor I, Laguna State Polytechnic University, Los Baños, Laguna, Philippines
Francis F. Balahadia. Assistant Professor II, Laguna State Polytechnic University, Siniloan, Laguna, Philippines
Gene Marck B. Catedrilla. Instructor I, Laguna State Polytechnic University, Los Baños, Laguna, Philippines
Mia V. Villarica. Assistant Professor I, Laguna State Polytechnic University, Sta. Cruz, Laguna, Philippines
Cite this article:
Asor, J.R., Balahadia, F.F., Catedrilla, G.B. & Villarica, M.V. (2022). Building model for crime pattern analysis through machine learning using predictive analytics. International Journal of Science, Technology, Engineering and Mathematics, 2(1), 61- 73. https://doi.org/10.53378/352875
License:
This work is licensed under a Creative Commons Attribution (CC BY 4.0) International License.