{"type":"ScholarlyArticle","title":"Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction","authors":[{"name":"Fei Yi","slug":"fei-yi","given":"Fei","family":"Yi","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , China; Baidu Inc , The Business Intelligence Lab , Baidu Research"},{"name":"Zhiwen Yu","slug":"zhiwen-yu","given":"Zhiwen","family":"Yu","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , China"},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":"Key Lab of IIP of CAS , Institute of Computing Technology , CAS , Beijing , China; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Bin Guo","slug":"bin-guo","given":"Bin","family":"Guo","affiliation":"Northwestern Polytechnical University , Xi'an , Shaanxi , China"}],"abstract":"Crime prediction has always been a crucial issue for public safety, and recent works have shown the effectiveness of taking spatial correlation, such as region similarity or interaction, for fine-grained crime modeling. In our work, we seek to reveal the relationship across regions for crime prediction using Continuous Conditional Random Field (CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance. We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods.","doi":"10.24963/ijcai.2019/577","url":"https://ijcai.org/proceedings/2019/577","pdf":"https://ijcai.org/proceedings/2019/0577.pdf","markdown":"https://ijcai.org/proceedings/2019/577.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/577","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":577,"pages":{"first":4157,"last":4163},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning Applications: Other Applications","Machine Learning: Learning Graphical Models"],"videos":[],"references":[{"position":1,"raw":"[Baltrušaitis et al., 2014] Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. Continuous conditional neu- ral fields for structured regression. In European conference on computer vision, pages 593-608. Springer, 2014.","title":"Continuous conditional neural fields for structured regression","authors":"Tadas Baltrušaitis, Peter Robinson, Louis-Philippe Morency","year":"2014","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chollet, 2017] Francois Chollet. Deep learning with python. Manning Publications Co., 2017.","title":"Deep learning with python","authors":"Francois Chollet","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Du et al., 2016] Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, and Hui Xiong. Catch me if you can: Detecting pickpocket suspects from large-scale transit records. In Proceedings of the 22nd ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, pages 87-96. ACM, 2016.","title":"Catch me if you can: Detecting pickpocket suspects from large-scale transit records","authors":"Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Fu et al., 2014] Yanjie Fu, Yong Ge, Yu Zheng, Zijun Yao, Yanchi Liu, Hui Xiong, and Jing Yuan. Sparse real estate ranking with online user reviews and offline moving be- haviors. In 2014 IEEE International Conference on Data Mining, pages 120-129. IEEE, 2014.","title":"Sparse real estate ranking with online user reviews and offline moving behaviors","authors":"Yanjie Fu, Yong Ge, Yu Zheng, Zijun Yao, Yanchi Liu, Hui Xiong, Jing Yuan","year":"2014","venue":"IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gers et al., 1999] Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. Learning to forget: Continual prediction with lstm. 1999.","title":"Learning to forget: Continual prediction with lstm","authors":"Jürgen Felix A Gers, Fred Schmidhuber, Cummins","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Koller and Friedman, 2009] Daphne Koller and Nir Fried- man. Probabilistic graphical models: principles and tech- niques. MIT press, 2009.","title":"Probabilistic graphical models: principles and techniques","authors":"Friedman Koller","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Krähenbühl and Koltun, 2011] Philipp Krähenbühl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. In Advances in neural information processing systems, pages 109-117, 2011.","title":"Philipp Krähenbühl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials","authors":"Koltun Krähenbühl","year":"2011","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu et al., 2016] Junming Liu, Leilei Sun, Weiwei Chen, and Hui Xiong. Rebalancing bike sharing systems: A multi-source data smart optimization. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1005- 1014. ACM, 2016.","title":"Rebalancing bike sharing systems: A multi-source data smart optimization","authors":"Junming Liu, Leilei Sun, Weiwei Chen, Hui Xiong","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2019] Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, and Hui Xiong. Joint representation learning for multi-modal transportation recommendation. AAAI, to appear, 2019.","title":"Joint representation learning for multi-modal transportation recommendation","authors":"Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong","year":"2019","venue":"AAAI, to appear","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Montgomery et al., 2012] Douglas C Montgomery, Eliza- beth A Peck, and G Geoffrey Vining. Introduction to lin- ear regression analysis, volume 821. John Wiley & Sons, 2012.","title":"Introduction to linear regression analysis","authors":"Elizabeth A Douglas C Montgomery, Geoffrey Peck, Vining","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Qin et al., 2009] Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. Global ranking using con- tinuous conditional random fields. In Advances in neural information processing systems, pages 1281-1288, 2009.","title":"Global ranking using continuous conditional random fields","authors":"Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li","year":"2009","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ristovski et al., 2013] Kosta Ristovski, Vladan Radosavlje- vic, Slobodan Vucetic, and Zoran Obradovic. Continuous conditional random fields for efficient regression in large fully connected graphs. In AAAI, pages 840-846, 2013.","title":"Continuous conditional random fields for efficient regression in large fully connected graphs","authors":"Kosta Ristovski, Vladan Radosavljevic, Slobodan Vucetic, Zoran Obradovic","year":"2013","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Vincent et al., 2010] Pascal Vincent, Hugo Larochelle, Is- abelle Lajoie, Yoshua Bengio, and Pierre-Antoine Man- zagol. Stacked denoising autoencoders: Learning use- ful representations in a deep network with a local de- noising criterion. Journal of machine learning research, 11(Dec):3371-3408, 2010.","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","authors":"Vincent","year":"2010","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Wang et al., 2013] Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri. Learning to detect patterns of crime. In Joint European conference on machine learning and knowledge discovery in databases, pages 515-530. Springer, 2013.","title":"Learning to detect patterns of crime","authors":"Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri","year":"2013","venue":"Joint European conference on machine learning and knowledge discovery in databases","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Wang et al., 2016] Hongjian Wang, Daniel Kifer, Corina Graif, and Zhenhui Li. Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD interna- tional conference on knowledge discovery and data min- ing, pages 635-644. ACM, 2016.","title":"Crime rate inference with big data","authors":"Hongjian Wang, Daniel Kifer, Corina Graif, Zhenhui Li","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2017] Liang Wang, Zhiwen Yu, Bin Guo, Tao Ku, and Fei Yi. Moving destination prediction using sparse dataset: A mobility gradient descent approach. ACM Transactions on Knowledge Discovery from Data, 11(3):37, 2017.","title":"Moving destination prediction using sparse dataset: A mobility gradient descent approach","authors":"Liang Wang, Zhiwen Yu, Bin Guo, Tao Ku, Fei Yi","year":"2017","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Xu et al., 2017] Dan Xu, Elisa Ricci, Wanli Ouyang, Xiao- gang Wang, and Nicu Sebe. Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In Proceedings of CVPR, volume 1, 2017.","title":"Multi-scale continuous crfs as sequential deep networks for monocular depth estimation","authors":"Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe","year":"2017","venue":"Proceedings of CVPR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Yi et al., 2018] Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, and Hui Xiong. An integrated model for crime pre- diction using temporal and spatial factors. In 2018 IEEE International Conference on Data Mining, pages 1386- 1391. IEEE, 2018.","title":"An integrated model for crime prediction using temporal and spatial factors","authors":"Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, Hui Xiong","year":"2018","venue":"2018 IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yu et al., 2015] Zhiwen Yu, Huang Xu, Zhe Yang, and Bin Guo. Personalized travel package with multi-point- of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Sys- tems, 46(1):151-158, 2015.","title":"Personalized travel package with multi-pointof-interest recommendation based on crowdsourced user footprints","authors":"Zhiwen Yu, Huang Xu, Zhe Yang, Bin Guo","year":"2015","venue":"IEEE Transactions on Human-Machine Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yu et al., 2016] Zhiwen Yu, Miao Tian, Zhu Wang, Bin Guo, and Tao Mei. Shop-type recommendation leverag- ing the data from social media and location-based services. ACM Transactions on Knowledge Discovery from Data, 11(1):1, 2016.","title":"Shop-type recommendation leveraging the data from social media and location-based services","authors":"Zhiwen Yu, Miao Tian, Zhu Wang, Bin Guo, Tao Mei","year":"2016","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yu et al., 2018] Zhiwen Yu, Fei Yi, Qin Lv, and Bin Guo. Identifying on-site users for social events: Mobility, con- tent, and social relationship. IEEE Transactions on Mobile Computing, 17(9):2055-2068, 2018.","title":"Identifying on-site users for social events: Mobility, content, and social relationship","authors":"Zhiwen Yu, Fei Yi, Qin Lv, Bin Guo","year":"2018","venue":"IEEE Transactions on Mobile Computing","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhao and Tang, 2017] Xiangyu Zhao and Jiliang Tang. Modeling temporal-spatial correlations for crime predic- tion. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 497-506. ACM, 2017.","title":"Modeling temporal-spatial correlations for crime prediction","authors":"Tang Zhao, Xiangyu Zhao, Jiliang Tang","year":"2017","venue":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zheng et al., 2015] Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr. Condi- tional random fields as recurrent neural networks. In Pro- ceedings of the IEEE international conference on com- puter vision, pages 1529-1537, 2015.","title":"Conditional random fields as recurrent neural networks","authors":"Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip Hs Torr","year":"2015","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}