Prediction of crime tendency of high-risk personnel using C5.0 decision tree empowered by particle swarm optimization

Chunxue Wu; Fang Yang; Yan Wu; Ren Han; Chunxue Wu; Fang Yang; Yan Wu; Ren Han

doi:10.3934/mbe.2019206

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4135-4150. doi: 10.3934/mbe.2019206

Previous Article Next Article

Research article Special Issues

Prediction of crime tendency of high-risk personnel using C5.0 decision tree empowered by particle swarm optimization

1.
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
2.
The School of Public and Environmental Affairs, Indiana University, Bloomington, USA

Received: 25 January 2019 Accepted: 08 May 2019 Published: 13 May 2019

The research on the big data in the security and protection industry has been increasingly recognized as the hotspot in case of the rapid development of the big data. This paper mainly focuses on addressing the problem that predicts the criminal tendency of the high-risk personnel based on the recorded behavior data of the high-risk personnel. Therefore, we propose a novel predictive model that is the crime tendency of high-risk personnel using C5.0 based on particle swarm optimization. In this model, the C5.0 decision tree algorithm is first used as a classifier, in which repeated tenfold cross-validation is used and then continuously tuned according to the custom fitness function based on particle swarm optimization. In addition, the classification accuracy, the reduced number of feature subset, specificity and sensitivity under different algorithms are compared. Finally, the proposed model has higher accuracy through the optimal value of the particle position, the error rate of the cost under different iterations and the trend and the concavity and convexity of ROC curve. The experimental results show that the proposed model has a good effect on the predictive classification, which may provide guidance for predicting crime tendency of high-risk personnel.
- big data,
- high-risk personnel,
- particle swarm optimization,
- C5.0,
- K-fold cross-validation
Citation: Chunxue Wu, Fang Yang, Yan Wu, Ren Han. Prediction of crime tendency of high-risk personnel using C5.0 decision tree empowered by particle swarm optimization[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4135-4150. doi: 10.3934/mbe.2019206

Related Papers:

Abstract

The research on the big data in the security and protection industry has been increasingly recognized as the hotspot in case of the rapid development of the big data. This paper mainly focuses on addressing the problem that predicts the criminal tendency of the high-risk personnel based on the recorded behavior data of the high-risk personnel. Therefore, we propose a novel predictive model that is the crime tendency of high-risk personnel using C5.0 based on particle swarm optimization. In this model, the C5.0 decision tree algorithm is first used as a classifier, in which repeated tenfold cross-validation is used and then continuously tuned according to the custom fitness function based on particle swarm optimization. In addition, the classification accuracy, the reduced number of feature subset, specificity and sensitivity under different algorithms are compared. Finally, the proposed model has higher accuracy through the optimal value of the particle position, the error rate of the cost under different iterations and the trend and the concavity and convexity of ROC curve. The experimental results show that the proposed model has a good effect on the predictive classification, which may provide guidance for predicting crime tendency of high-risk personnel.

References

[1]	A. Mehmood, I. Natgunanathan, X. Yong, et al., Protection of big data privacy, IEEE Access, 4 (2016), 1821–1834.
[2]	H. Cai, B. Xu, L. Jiang, et al., IoT-based big data storage systems in cloud computing: perspectives and challenges, IEEE Internet Things, 4 (2017), 75–87.
[3]	H. Chen, W. Chung, J. J. Xu, et al., Crime data mining: A general framework and some examples, Computer, 37 (2004), 50–56.
[4]	P. Hanchuan, L. Fuhui and D. Chris, Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, IEEE T. Pattern Anal., 27 (2005), 1226–1238.
[5]	Y. Liu, Z. Qin, Z. Xu, et al., Feature selection with particle swarms, Comput. Inf. Proceedings, 3314 (2004), 425–430.
[6]	M. Agaoglu, Predicting instructor performance using data mining techniques in higher education, IEEE Access, 4 (2016), 2379–2387.
[7]	T. Almanie, R. Mirza and E. Lor, Crime prediction based on crime types and using spatial and temporal criminal hotspots, Comput. Sci., 5 (2015), 70–89.
[8]	S. C. Jeeva and E. B. Rajsingh, Intelligent phishing url detection using association rule mining, Hum. Cent. Comput. Inf. Sci., 6 (2016), 1–19.
[9]	R. Sujatha and D. Ezhilmaran, A new efficient SIF-based FCIL (SIFâFCIL) mining algorithm in predicting the crime locations, J. Exp. Theor. Artif. In., 28 (2015), 561–579.
[10]	V. Ingilevich and S. Ivanov, Crime rate prediction in the urban environment using social factors, Procedia Comput. Sci., 136 (2018), 472–478.
[11]	M. A. Jalil, F. Mohd and N. Maizura, A comparative study to evaluate filtering methods for crime data feature selection, Procedia Comput. Sci., 116 (2017), 113–120.
[12]	O. Kotevska, A. G. Kusne, D. V. Samarov, et al., Dynamic network model for smart city data-loss resilience case study: City-to-city network for crime analytics, IEEE Access, 5 (2017), 20524–20535.
[13]	K. Konstantina, T. P. Exarchos, K. P. Exarchos, et al., Machine learning applications in cancer prognosis and prediction, Comput. Struct. Biot., 13 (2015), 8–17.
[14]	C. Mao, R. Lin, C. Xu, et al., Towards a trust prediction framework for cloud services based on PSO-driven neural network, IEEE Access, 5 (2017), 2187–2199.
[15]	S. M. Vieira, L. F. Mendonça, G. J. Farinha, et al., Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients, Appl. Soft Comput., 13 (2013), 3494–3504.
[16]	C. Wu, C. Luo, N. Xiong, et al., A greedy deep learning method for medical disease analysis, IEEE Access, 6 (2018), 20021–20030.
[17]	H. Wu, S. Yang, Z. Huang, et al., Type 2 diabetes mellitus prediction model based on data mining, Inform. Med. Unlocked, 10 (2018), 100–107.
[18]	H. Li, X. Mao, C. Wu, et al., Design and analysis of a general data evaluation system based on social networks, Eurasip Wireless Commu. Netw., 1 (2018), 109–120.
[19]	X. Meng, Y. Huang, D. Rao, et al., Comparison of three data mining models for predicting diabetes or prediabetes by risk factors, Kaohsiung Med. Sci., 29 (2013), 93–99.
[20]	Y. Shen, C. Wu, C. Liu, et al., Oriented feature selection SVM applied to cancer prediction in precision medicine, IEEE Access, 6 (2018), 48510–48521.
[21]	F. Han, C. Yang, Y. Wu, et al., A gene selection method for microarray data based on binary PSO encoding gene-to-class sensitivity information, IEEE/ACM Trans. Comput. Biol. Bioinformat., 14 (2017), 85–96.
[22]	S. M. Vieira, J. Sousa and T. A. Runkler, Two cooperative ant colonies for feature selection using fuzzy models, Expert Syst. Appl., 37 (2010), 2714–2723.
[23]	W. Ke, C. Wu, Y. Wu, et al., A new filter feature selection based on criteria fusion for gene microarray data, IEEE Access, 6 (2018), 61065–61076.
[24]	J. Kennedy and R. C. Eberhart, A discrete binary version of the particle swarm algorithm, 1997 IEEE International Conference on Systems, Man, and Cybernetics.Computational Cybernetics and Simulation, 2002.
[25]	S. R. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE T. Syst. Man Cybern. B Cybern, 21(1991), 660–674.
[26]	P. A. Chou, Optimal partitioning for classification and regression trees, IEEE T. Pattern Anal., 13 (1991), 340–354.
[27]	J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc, 1992.
[28]	H. Masnadi-Shirazi and N. Vasconcelos, Cost-Sensitive Boosting, IEEE T. Pattern Anal., 33 (2011), 294–309.
[29]	W. Hu, W. Hu and S. Maybank, AdaBoost-based algorithm for network intrusion detection, IEEE T. Syst. Man Cybern. B Cybern, 38 (2008), 577–583.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)