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-  2018 

基于贝叶斯决策的网格社区案卷分发模型
Grid community case classification and distribution model based on Bayesian decision

DOI: 10.6040/j.issn.1671-9352.3.2018.001

Keywords: 网格社区,贝叶斯决策,大数据,案卷分发,
grid community
,Bayesian decision,big data,case distribution

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Abstract:

摘要: 随着我国城市化进程不断深入,智慧城市与合作治理正日益成为发展的新范式,而信息技术和智能终端设备的普及应用也使得全民参与社会公共管理成为可能。传统的群众与政府间沟通渠道和社会管理平台架构已难以满足不断增长的数据规模和群众广泛参与城市治理的社会现实。因此,提出了一种基于贝叶斯决策的网格社区案卷分发模型。模型首先运用贝叶斯决策理论对群众上报社管案卷信息进行分析并归类,然后结合案卷上报地理位置信息确定其所在社区网格,最后根据分类结果将案卷分发至所属社区网格的相应职能部门。K-fold交叉验证结果表明,提出的案卷分发模型具有较好的可用性和准确性。
Abstract: Along with the intensification of urbanization in China, smart city and collaborative governance are becoming the novel paradigm of development. In the meantime, popularization of information technology and smart end devices makes it possible for civilians to widely participate in social public management. However, traditional channels of communication between people and government and the community management platform architecture have failed to meet the increasingly growing scale of data and the social reality that civilians are broadly engaging in urban governance. Hence, the grid community case classification and distribution model based on the Bayesian decision is proposed in this study. Firstly, the model adopted uses the theory of Bayesian decision to analyze and classify the social management case information that civilians have handed in. Then, involving the location information as the cases report, it ensures the certain social grid where exactly it is. Consequently, cases are to be delivered to the relevant departments of the social grid to which it belongs in terms of the classification results. K-fold cross-validation results show that the case distribution model proposed in the study has high availability and accuracy

References

[1]  SAHAMI M, HEILMAN T D. A web-based kernel function for measuring the similarity of short text snippets[C] // International Conference on World Wide Web, WWW 2006. Edinburgh: ACM, 2006: 377-386.
[2]  IRANI D, WEBB S, PU C, et al. Study of trend-stuffing on twitter through text classification[C] // CEAS. Washington: ACM, 2010: 46-54.
[3]  KUMBHAR P, MALI M, ATIQUE M. A genetic-fuzzy approach for automatic text categorization[C] // Advance Computing Conference(IACC), 2017 IEEE 7th International. Hyderabad: IEEE, 2017: 572-578.
[4]  LI Yuefeng, ZHANG Libiao, XU Yue, et al. Enhancing binary classification by modeling uncertain boundary in three-way decisions[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(7):1438-1451.
[5]  徐漪,沈建峰.大数据时代社会治理的变革:模式与策略[J].产业与科技论坛,2017,16(24):9-11. XU Yi, SHEN Jianfeng. The transformation of social governance in the age of big data: patterns and strategies[J]. Industrial & Science Tribune, 2017, 16(24):9-11.
[6]  黄亚坤,王杨,王明星. 综合社区与关联序列挖掘的电子政务推荐算法[J]. 计算机应用, 2017, 37(9): 2671-2677. HUANG Yakun, WANG Yang, WANG Mingxing. E-government recommendation algorithm combining community and association sequence mining[J]. Journal of Computer Applications, 2017, 37(9):2671-2677.
[7]  周庆平,谭长庚,王宏君,等. 基于聚类改进的KNN文本分类算法[J]. 计算机应用研究, 2016, 33(11):3374-3377. ZHOU Qingping, TAN Changgeng, WANG Hongjun, et al. Improved KNN text classification algorithm based on clustering[J]. Application Research of Computers, 2016, 33(11):3374-3377.
[8]  周俊,郑中华,张炜. 基于改进最大匹配算法的中文分词粗分方法[J]. 计算机工程与应用, 2014, 50(2):124-128. ZHOU Jun, ZHENG Zhonghua, ZHANG Wei. Chinese word segmentation based on improving maximum matching algorithm[J]. Computer Engineering and Applications, 2014, 50(2):124-128.
[9]  MEIJER A,BOLíVAR M P R. Governing the smart city: a review of the literature on smart urban governance[J]. International Review of Administrative Sciences, 2015, 82(2):392-408.
[10]  SRIRAM B, FUHRY D, DEMIR E, et al. Short text classification in twitter to improve information filtering[C] // International ACM SIGIR Conference on Research and Development in Information Retrieval. Geneva: ACM, 2010: 841-842.
[11]  LIU H, LI X, ZHANG S. Learning instance correlation functions for multilabel classification[J]. IEEE Transactions on Cybernetics, 2017, 47(2):499-510.
[12]  承孝敏. 大数据应用于社会治理的芜湖实践[J]. 社会治理, 2016(4):114-116. CHENG Xiaomin. The application of big data in the practice of social governance in Wuhu[J]. Social Governance Review, 2016(4):114-116.
[13]  黄昌宁,赵海. 中文分词十年回顾[J]. 中文信息学报, 2007, 21(3):8-19. HUANG Changning, ZHAO Hai. Chinese word segmentation: a decade review[J]. Journal of Chinese Information Processing, 2007, 21(3):8-19.
[14]  VEHTARI A, GELMAN A, GABRY J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC[J]. Statistics and Computing, 2017, 27(5):1413-1432.
[15]  王保森. 网格化管理: 城市社区管理模式的创新[J]. 规划师, 2007, 23(5):46-49. WANG Baosen. Grid management: innovation of urban community management model[J]. Planners, 2007, 23(5):46-49.
[16]  周志华.机器学习[M]. 北京: 清华大学出版社, 2016: 147-149. ZHOU Zhihua. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 147-149.
[17]  王青松,魏如玉.基于短语的贝叶斯中文垃圾邮件过滤方法[J]. 计算机科学, 2016, 43(4):256-259. WANG Qingsong, WEI Ruyu. Bayesian Chinese spam filtering method based on phrases[J]. Computer Science, 2016, 43(4):256-259.

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