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基于决策树的高校成人学籍查询方法研究

Research on Adult-student Status Querying Method Based on Decision Tree
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摘要 高校成人学生来源广、差别大,学籍也十分复杂。学籍检索是学籍管理中的主要工作,通过构造决策树来发现学籍信息中蕴含的分类规则,利用这些规则可以在学籍信息中快速准确地进行查询。提出了一种基于决策树的学籍信息查询方法,分析了决策树建立的过程,讨论了过度拟合的处理方式,此方法可以提高学籍信息查询的速度和质量。 The sources of adult students in colleges are wide and the differences of them are big, so their student status is very complex. The main work of student status management is student status retrieval that is to find the classification rules contained in student status information through constructing the decision tree and to use these rules to do querying rapidly and accurately in student status information. This paper puts forward a student status information querying method based on the decision tree, analyzes the process of constructing the decision tree, and discusses the treatment of the over fitting,which can be used to improve the quality and speed of student status information query.
作者 徐琦 潘庆超
出处 《山西科技》 2016年第3期82-85,共4页 Shanxi Science and Technology
关键词 高校成人学籍 查询方法 决策树 college student status querying method decision tree
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  • 1Wagner R A, Fischer M J. The string-to-string correction problem. Journal of the ACM, 1974, 21(1): 168-173.
  • 2Cormen T H, Leiserson C E, Rivest R L. Introduction to Algorithms. 2nd Edition. Cambridge, Massachusetts, USA: The MIT Press, 2002.
  • 3Masek W J, Paterson M. A faster algorithm computing edit distances. Journal of Computer and System Sciences, 1980, 20(1) : 18-31.
  • 4Cornode G, Muthukrishnan S. The string edit distance mat ching problem with moves. ACM Transactions on Algo- rithms, 2007, 3(1).
  • 5Gravano L, Ipeirotis P G, Jagsdish H Vet al. Approximate string join in a database (almost) for free//Proeeedings of the 27th International Conference on Very Large Data Bases. Roma, Italy, 20011 491-500.
  • 6Li Chen, Lu Jia-Heng, Lu Yi-Ming. Efficient merging and filtering algorithms for approximate string searehes//Pro- ceedings of the 24th International Conference on Data Engi- neering. Canefln, M6xico, 2008:257-266.
  • 7Sarawagi S, Kirpal A. Efficient set joins on similarity predicates//Proeeedings of the ACM SIGMOD International Conference on Management of Data. Paris, France, 2004:743-754.
  • 8Chaudhuri S, Ganti V, Kaushik R. A primitive operator for similarity ioins in data cleaning//Proceedings of the 22nd International Conference on Data Engineering. Atlanta, USA, 2006:5-15.
  • 9Xiao Chuan, Wang Wei, Lin Xue-Min. Ed-join: An efficient algorithm for similarity joins with edit distance constrains// Proceedings of the 34th International Conference on Very Large Data Bases. Auckland, New Zealand, 2008:933-944.
  • 10Behm A, Ji Sheng-Yue, Li Chen, Lu Jia-Heng. Pace-con strained gram-based indexing for efficient approximate string search//Proceedings of the 25th International Conference on Data Engineering. Shanghai, China, 2009: 204-215.

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