Etiketlenmiş Verilerin Kıtlığı Durumunda Suç Oluşumunun Tahmini

Year 2021, Volume: 23 Issue: 68, 677 - 687, 24.05.2021

Volkan Kıranoglu , Göksu Tüysüzoğlu , Elife Öztürk Kıyak

https://doi.org/10.21205/deufmd.2021236828

Cited By: 2

Abstract

Teknolojik gelişmeler doğrultusunda, makine öğrenmesi/veri madenciliği çalışmaları suç analizinde önemli ölçüde artmıştır. Suç olaylarının tahmini, ceza davalarının mekansal/zamansal dağılımının tespiti, suç türünün öngörülmesi bu çalışma alanlarından bazılarıdır. Suç oranlarındaki önemli artıştan kaynaklanan suç verileri dikkate alındığında, gelecekteki olaylar için suç kalıplarını araştırmak veya suçla ilgili tahminleri kolayca yapmak için etiketlenmemiş veriler kullanılabilir. Bu nedenle, bu çalışmada, 2019 polis verilerini kullanarak İngiltere'de suç araştırmalarının sonucunu tahmin etmek için aktif öğrenme, kendi kendine öğrenme ve rastgele örnekleme teknikleri uygulanmıştır. Deneysel analize göre, aktif öğrenme, çok az etiketlenmiş veri olması durumunda düzensizliğe dayalı akıllı seçim stratejisini kullanarak muadillerinden daha iyi performans göstermektedir.

Keywords

Aktif Öğrenme, Sınıflandırma, Suç Tespiti, Rastgele Örnekleme, Yarı Denetimli Öğrenme, Kendi Kendine Öğrenme

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Abstract

In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, self-learning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

Keywords

Active Learning, Classification, Crime Detection, Random Sampling, Semi-Supervised Learning, Self-Learning

References

• Shukla, S., Jain, P.K., Babu, C.R., Pamula, R. 2020. A Multivariate Regression Model for Identifying, Analyzing and Predicting Crimes, Wireless Personal Communications. DOI: 10.1007/s11277-020-07335-w
• Kadar, C., Pletikosa, I. Mining Large-Scale Human Mobility Data for Long-Term Crime Prediction 2018. EPJ Data Science, Volume. 7(26). DOI: 10.1140/epjds/s13688-018-0150-z
• Agrawal, S., Sejwar, V. 2017. Crime Identification using FP-Growth and Multi Objective Particle Swarm Optimization. IEEE International Conference on Trends in Electronics and Informatics, 11-12 May 2017, Tirunelveli, India, 727-734. DOI: 10.1109/ICOEI.2017.8300799
• Nitta, G.R., Rao, B.Y., Sravani, T., Ramakrishiah, N., BalaAnand, M. 2019. LASSO-Based Feature Selection and Naïve Bayes Classifier for Crime Prediction and Its Type, Service Oriented Computing and Applications, Volume. 13(3), p. 187-197. DOI: 10.1007/s11761-018-0251-3
• Tayal, D.K., Jain, A., Arora, S., Agarwal, S., Gupta, T., Tyagi, N. 2015. Crime Detection and Criminal Identification in India using Data Mining Techniques, AI & Society, Volume. 30(1), p. 117-127. DOI: 10.1007/s00146-014-0539-6
• Shermila, A.M., Bellarmine, A.B., Santiago, N. 2018. Crime Data Analysis and Prediction of Perpetrator Identity using Machine Learning Approach. IEEE 2nd International Conference on Trends in Electronics and Informatics, 11-12 May 2018, Tirunelveli, India, 107-114. DOI: 10.1109/ICOEI.2018.8553904
• Khan, J., Lee, Y.K. 2019, LeSSA: A Unified Framework Based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification, Applied Sciences, Volume. 9, p. 5562-5590. DOI: 10.3390/app9245562
• Iqbal, R., Murad, M.A.A., Mustapha, A., Panahy, P.H.S., Khanahmadliravi, N. 2013. An Experimental Study of Classification Algorithms for Crime Prediction, Indian Journal of Science and Technology, Volume. 6(3), p. 4219-4225. DOI: 10.17485/ijst/2013/v6i3.6
• 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, Volume. 8(2), p. 141-147. DOI: 10.12720/jait.8.2.141-147
• Alves, L.G., Ribeiro, H.V., Rodrigues, F.A. 2018. Crime Prediction through Urban Metrics and Statistical Learning. Physica A: Statistical Mechanics and its Applications, Volume. 505, p. 435-443. DOI: 10.1016/j.physa.2018.03.084
• Chhabra G., Vashisht V., Ranjan J., Crime Prediction Patterns Using Hybrid K-Means Hierarchical Clustering. Journal of Advanced Research in Dynamical Control Systems, Volume. 11, p. 1249-1258
• Sivanagaleela B., Rajesh S. 2019. Crime Analysis and Prediction using Fuzzy C-Means Algorithm. IEEE 3rd International Conferences on Trends in Electronics and Informatics (ICOEI), 23-25 April, Tirunelveli, 595-599. DOI: 10.1109/ICOEI.2019.8862691
• Masood, A., Al-Jumaily, A., Anam, K. 2015. Self-Supervised Learning Model for Skin Cancer Diagnosis. 7th International IEEE/EMBS Conference on Neural Engineering (NER), 22-24 April, Montpellier, 1012-1015. DOI: 10.1109/NER.2015.7146798
• Esparza J., Scherer S., Schwenker F. 2012. Studying Self- and Active-Training Methods for Multi-Feature Set Emotion Recognition. pp 19-31. Schwenker F., Trentin E., ed. 2011. Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science, vol 7081. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-28258-4_3
• Khan, J., Lee, Y.K. 2019. LeSSA: A Unified Framework Based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification. Applied Sciences, Volume. 9(24). DOI: 10.3390/app9245562
• Karlos, S., Kanas, V.G., Aridas, C., Fazakis, N., Kotsiantis, S. 2019. Combining Active Learning with Self-train algorithm for Classification of Multimodal Problems. IEEE 10th International Conference on Information, Intelligence, System and Applications (IISA), 15-17 July, Patras, Greece, 1-8. DOI: 10.1109/IISA.2019.8900724
• Li, F., Qu, Y., Ji, J., Zhang, D., Li, L. 2020. Active Learning Empirical Research on Cross-Version Software Defect Prediction Datasets. International Journal of Performability Engineering, Volume. 16(4), p. 609-617. DOI: 10.23940/ijpe.20.04.p12.609617
• Kellenberger, B., Marcos, D., Lobry, S., Tuia, D. 2019. Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery Using Deep CNNs and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, Volume. 57(12), p. 9524-9533. DOI: 10.1109/TGRS.2019.2927393
• Vitório, D., Souza, E., Oliveira, A.L. 2019. Using Active Learning Sampling Strategies for Ensemble Generation on Opinion Mining. 8th Brazilian Conference on Intelligent Systems (BRACIS), 15-18 October, Salvador, Brazil, 114-119. DOI: 10.1109/BRACIS.2019.00029
• Han, W., Coutinho, E., Ruan, H., Li, H., Schuller, B., Yu, X., Zhu, X. 2016. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments. PloS one, Volume. 11(9). DOI: 10.1371/journal.pone.01620
• Nath, S.V. 2006. Crime Pattern Detection Using Data Mining. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 18-22 December, Hong Kong, China, 41-44. DOI: 10.1109/WI-IATW.2006.55