麦晓冬, 贾 萍, 翁建荣, 彭凌西. 基于多尺度粗糙集模型的决策树在高校就业数据分析中的应用[J]. 华南师范大学学报(自然科学版), 2014, 46(4): 9. doi: 10.6054/j.jscnun.2014.06.105
引用本文: 麦晓冬, 贾 萍, 翁建荣, 彭凌西. 基于多尺度粗糙集模型的决策树在高校就业数据分析中的应用[J]. 华南师范大学学报(自然科学版), 2014, 46(4): 9. doi: 10.6054/j.jscnun.2014.06.105
Mai Xiaodong, Jia Ping, Weng Jianrong, Peng Lingxi. DATA MINING IN EMPLOYMENT BASED ON MULTISCALE ROUGH SET MODEL DECISION TREE[J]. Journal of South China Normal University (Natural Science Edition), 2014, 46(4): 9. doi: 10.6054/j.jscnun.2014.06.105
Citation: Mai Xiaodong, Jia Ping, Weng Jianrong, Peng Lingxi. DATA MINING IN EMPLOYMENT BASED ON MULTISCALE ROUGH SET MODEL DECISION TREE[J]. Journal of South China Normal University (Natural Science Edition), 2014, 46(4): 9. doi: 10.6054/j.jscnun.2014.06.105

基于多尺度粗糙集模型的决策树在高校就业数据分析中的应用

DATA MINING IN EMPLOYMENT BASED ON MULTISCALE ROUGH SET MODEL DECISION TREE

  • 摘要: 为解决目前常用于就业数据分析的C4.5算法、基于粗糙集等的决策树生成算法均无法很好地处理决策精度需求不同和噪声适应能力的问题,运用基于多尺度粗糙集模型的决策树算法于于高校就业数据分析,并以某高校2012年就业数据为例进行分析,同时将分析结果与C4.5算法和基于粗糙集的决策树生成算法的分析结果进行比较.结果表明:基于多尺度粗糙集模型的决策树算法生成的决策树树形结构简单、产生的规则简洁、不存在不可分的数据集、运算速度快.

     

    Abstract: In order to solve different requirements for decision accuracy and noise adaptation, but some decision tree algorithms by C4.5 and base on Rough Set(RS) are used in the analysis of employment data, are unable to solve those problems,this paper present the algorithm to build decision tree based on Multiscale Rough Set Mode into employment data analysis,do a test on employment data of a university in 2012 for training set, and compare results with C4.5 and base on RS. The results show that decision treeconstructed by MRSM is simple, and decision-making rules are Concise, and the data set is end classification, and faster computing speed.

     

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