您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(理学版)》

山东大学学报 (理学版) ›› 2018, Vol. 53 ›› Issue (11): 85-94.doi: 10.6040/j.issn.1671-9352.3.2018.001

• • 上一篇    

基于贝叶斯决策的网格社区案卷分发模型

王鹤琴1,王杨2   

  1. 1.安徽警官职业学院信息管理系, 安徽 合肥 230031;2.安徽师范大学计算机与信息学院, 安徽 芜湖 241000
  • 发布日期:2018-11-14
  • 作者简介:王鹤琴(1979— ),女,硕士,副教授,研究方向为WEB应用开发、数据挖掘、个性化推荐. E-mail:39685280@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61572036);安徽省重大人文社科基金资助项目(SK2014ZD033);安徽省高校自然科学研究重点项目(KJ2016A167);安徽省高等学校自然科学研究重点项目(KJ2017A639)

Grid community case classification and distribution model based on Bayesian decision

WANG He-qin1, WANG Yang2   

  1. 1. Department of Information Management, Anhui Vocational College of Police Officers, Hefei 230031, Anhui, China;
    2.School of Computer and Information Technology, Anhui Normal University, Wuhu 241000, Anhui, China
  • Published:2018-11-14
  • About author:国家自然科学基金资助项目(61572036);安徽省重大人文社科基金资助项目(SK2014ZD033);安徽省高校自然科学研究重点项目(KJ2016A167);安徽省高等学校自然科学研究重点项目(KJ2017A639)
  • Supported by:
    国家自然科学基金资助项目(61572036);安徽省重大人文社科基金资助项目(SK2014ZD033);安徽省高校自然科学研究重点项目(KJ2016A167);安徽省高等学校自然科学研究重点项目(KJ2017A639)

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。

关键词: 网格社区, 贝叶斯决策, 大数据, 案卷分发

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.

Key words: grid community, Bayesian decision, big data, case distribution

中图分类号: 

  • TP393
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] IRANI D, WEBB S, PU C, et al. Study of trend-stuffing on twitter through text classification[C] // CEAS. Washington: ACM, 2010: 46-54.
[7] 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.
[8] 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.
[9] 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.
[10] LIU H, LI X, ZHANG S. Learning instance correlation functions for multilabel classification[J]. IEEE Transactions on Cybernetics, 2017, 47(2):499-510.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[1] 晏燕,郝晓弘. 差分隐私密度自适应网格划分发布方法[J]. 山东大学学报(理学版), 2018, 53(9): 12-22.
[2] 刘利钊,于佳平,刘健,李俊祎,韩哨兵,许华荣,林怀钏,朱顺痣. 基于量子辐射场的大数据安全存储寻址算法[J]. 山东大学学报(理学版), 2018, 53(7): 65-74.
[3] 李金海,吴伟志. 形式概念分析的粒计算方法及其研究展望[J]. 山东大学学报(理学版), 2017, 52(7): 1-12.
[4] 冶建华,马明*,刘华. 基于贝叶斯分析的营销决策[J]. J4, 2012, 47(3): 98-102.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 邵勇. 半格序完全正则周期半群[J]. 山东大学学报(理学版), 2018, 53(10): 1 -5 .
[2] 巩增泰,高寒. n维模糊数值函数的预不变凸性[J]. 山东大学学报(理学版), 2018, 53(10): 72 -81 .
[3] 陈文倩,张孝金,昝立博. Gorenstein代数上的倾斜模的个数[J]. 山东大学学报(理学版), 2018, 53(10): 14 -16 .
[4] 郭寿桃,王占平. 正合零因子下模的Gorenstein同调维数[J]. 山东大学学报(理学版), 2018, 53(10): 17 -21 .
[5] 吴小英,王芳贵. 分次版本的Enochs定理[J]. 山东大学学报(理学版), 2018, 53(10): 22 -26 .
[6] 李美莲,邓青英. 平图的transition多项式的Maple计算[J]. 山东大学学报(理学版), 2018, 53(10): 27 -34 .
[7] 王丹,王正攀. 用禁止子半群刻画带簇的一个真子簇[J]. 山东大学学报(理学版), 2018, 53(10): 6 -8 .
[8] 梁星亮,吴苏朋,任军. C(P')系对幺半群的刻画[J]. 山东大学学报(理学版), 2018, 53(10): 9 -13 .
[9] 房启明,张莉. 无4-圈和5-圈的平面图的k-frugal列表染色[J]. 山东大学学报(理学版), 2018, 53(10): 35 -41 .
[10] 甄苇苇,曾剑,任建龙. 基于变分理论与时间相关的抛物型反源问题[J]. 山东大学学报(理学版), 2018, 53(10): 61 -71 .