[1]胡来丰,舒兰. 基于粗集理论的决策树在信用卡发放中的应用[J].计算机技术与发展,2015,25(03):142-145.
　HU Lai-feng,SHU Lan. Application of Decision Tree Based on Rough Set Theory in Credit Card Payment[J].,2015,25(03):142-145.
点击复制

 基于粗集理论的决策树在信用卡发放中的应用()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年03期
页码:
142-145
栏目:
应用开发研究
出版日期:
2015-03-10

文章信息/Info

Title:
 Application of Decision Tree Based on Rough Set Theory in Credit Card Payment
文章编号:
1673-629X（2015)03-0142-04
作者:
 胡来丰; 舒兰
 电子科技大学 数学科学学院
Author(s):
 HU Lai-feng; SHU Lan
关键词:
 ; 粗糙集; 属性约简; J48决策树; 交叉验证率
Keywords:
 ; rough set; attribute reduction; J48 decision tree; cross validation rate
分类号:
TP183
文献标志码:
A
摘要:
 基于粗集和决策树两种方法的各自优势互补，提出将粗集与决策树相结合的新方法，并将此算法运用到个人信用卡发放模型中。首先利用布尔推理算法将连续属性进行离散化处理，然后采用一种以加权和属性重要度为启发信息进行属性约简，得到降维数据，最后采用J48决策树算法，得到决策规则。通过对比K最近邻分类、朴素贝叶斯、RBF神经网络、支持向量机等算法，这种新的数据挖掘算法保留了原有数据特点，加快了知识获取的进程，提高了模型的交叉验证率，简化了规则，取得了满意的研究结果。
Abstract:
 Based on the complementary advantages of rough set and decision tree method,put forward a new method of combining rough set and decision tree,and use this algorithm to personal credit card payment model. Firstly,use Boolean reasoning algorithm to continuous attribute for discretization processing,and apply a heuristic information of weighted and attribute importance for attribute reduction to ob-tain data with dimensionality reduction,finally utilize J48 decision tree algorithm to get the decision rules. Compared with the K nearest neighbor classification,Naive Bayes,RBF neural networks,support vector machines and other types of algorithms,this new data mining algorithm retains the original data characteristics to accelerate the process of knowledge acquisition,improving cross-model verification rate,simplifying the rules,getting the satisfactory results.

相似文献/References:

[1]夏奇思 王汝传.基于属性约简的粗糙集海量数据分割算法研究[J].计算机技术与发展,2010,(04):5.
　XIA Qi-si,WANG Ru-chuan.Mass Data Partition for Rough Set on Attribute Reduction Algorithm[J].,2010,(03):5.
[2]张政超 关欣[] 何友 李应升 郭伟峰.粗糙集理论数据处理方法及其研究[J].计算机技术与发展,2010,(04):12.
　ZHANG Zheng-chao,GUAN Xin[],HE You,et al.Rough Sets Data Processing Method and Its Research[J].,2010,(03):12.
[3]杨乐婵 邓松 徐建辉.基于BP网络的洪灾风险评价算法[J].计算机技术与发展,2010,(04):232.
　YANG Le-chan,DENG Song,XU Jian-hui.Flood Risk Evaluation Algorithm on BP Net[J].,2010,(03):232.
[4]张学友 苗强 毛军军.基于粗糙度的一种分形维数计算方法[J].计算机技术与发展,2010,(05):136.
　ZHANG Xue-you,MIAO Qiang,MAO Jun-jun.A Calculation Method of Fractal Dimension Based on Roughness[J].,2010,(03):136.
[5]王伟 高亮 吴涛.粗糙集在经济分析中的应用[J].计算机技术与发展,2008,(04):158.
　WANG Wei,GAO Liang,WU Tao.Application of Rough Set in Economic Analysis[J].,2008,(03):158.
[6]李学文 王小刚.优势信息系统的属性约简算法[J].计算机技术与发展,2009,(08):107.
　LI Xue-wen,WANG Xiao-gang.Algorithm on Attribute Reduction in Dominance Information System Based on Dominance Relation[J].,2009,(03):107.
[7]徐沈 吴涛[] 李国成.产业结构调整的量化分析[J].计算机技术与发展,2009,(08):178.
　XU Shen,WU Tao,LI Guo-cheng.Quantitative Analysis on Adjustment of Industrial Structure[J].,2009,(03):178.
[8]申锦标 吕跃进.粗糙集的近似约简及其算法[J].计算机技术与发展,2009,(12):17.
　SHEN Jin-biao,LU Yue-jin.A Rough Set of Approximate Attribute Reduction and Its Algorithm[J].,2009,(03):17.
[9]王小菊 蒋芸 李永华.基于依赖度之差的属性重要性评分[J].计算机技术与发展,2009,(01):67.
　WANG Xiao-ju,JIANG Yun,LI Yong-hua.Significance of Attribute Evaluation Based on Dependable Difference[J].,2009,(03):67.
[10]汪小燕 杨思春.基于改进的二进制可辨矩阵的核增量式更新方法[J].计算机技术与发展,2009,(01):97.
　WANG Xiao-yan,YANG Si-chun.An Incremental Updating Approach to Compute a Core Based on Improved Binary Discernable Matrix[J].,2009,(03):97.
[11]鄂旭[],杨健[],王欣铨[],等. 水产品安全信息系统中属性离散化方法研究[J].计算机技术与发展,2014,24(07):178.
　E Xu[],YANG Jian[],WANG Xin-quan[],et al. Research on Discretization Method in Aquatic Product Safety Information System[J].,2014,24(03):178.
[12]王添,姜麟,米允龙. 海量数据下不完备信息系统的知识约简算法[J].计算机技术与发展,2015,25(01):137.
　WANG Tian,JIANG Lin,MI Yun-long. Knowledge Reduction Algorithms of Incomplete Information System in Massive Datasets[J].,2015,25(03):137.
[13]徐怡[][],陈玉莹[],赵彦娇[],等. 基于粗糙集理论的高校学生就业意向研究[J].计算机技术与发展,2015,25(10):209.
　XU Yi[] [],CHEN Yu-ying[],ZHAO Yan-jiao[],et al. Research on College Students’ Employment Intention Based on Rough Set Theory[J].,2015,25(03):209.
[14]唐启涛,张燕,彭利红. 基于粗糙集约简算法的配置文本聚类方法研究[J].计算机技术与发展,2015,25(11):105.
　TANG Qi-tao,ZHANG Yan,PENG Li-hong. Research on Clustering Method of Configuration Text Based on Rough Sets Reduction Algorithm[J].,2015,25(03):105.
[15]闫之焕. Tableau算法在粗糙描述逻辑中的扩展应用[J].计算机技术与发展,2015,25(12):10.
　YAN Zhi-huan. Extension Application of Tableau Algorithm in Rough Description Logic[J].,2015,25(03):10.
[16]严静静,张腾飞. 基于自适应的粗糙C-均值聚类算法[J].计算机技术与发展,2016,26(03):67.
　YAN Jing-jing,ZHANG Teng-fei. Rough C-means Clustering Algorithm Based on Self-adaption[J].,2016,26(03):67.
[17]杨志勇,朱跃龙,万定生. 基于知识粒度的时间序列异常检测研究[J].计算机技术与发展,2016,26(07):51.
　YANG Zhi-yong,ZHU Yue-long,WAN Ding-sheng. Research on Time Series Anomaly Detection Based on Knowledge Granularity[J].,2016,26(03):51.
[18]蔡兴雨[],徐怡[][],程智炜[]. 基于粗糙集理论的影响高校学生成绩因素研究[J].计算机技术与发展,2016,26(11):200.
　CAI Xing-yu[],XU Yi[],CHENG Zhi-wei[]. Research on Factors Affecting College Achievement Based on Rough Set[J].,2016,26(03):200.

更新日期/Last Update: 2015-05-04