基于AIC的粗糙集择优方法
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摘要
在实际应用中,当利用多种粗糙集构造算法所得到的多个粗糙集的误判率差异小时,误判率小的粗糙集并不总是具有最高预测准确度。利用粗糙集的分类规则构建Logistic模型,将拟合Logistic模型的AIC值作为该粗糙集的AIC值,用于粗糙集的择优。实例分析结果表明,采用新方法能够筛选出预测准确度较高的粗糙集。当多个粗糙集的误判率差异小时,新方法更可能选出预测准确度最高的粗糙集。
In practical application,when there is small difference of misclassification error among many rough sets constructed by multiple rough set construction algorithms,the rough set with least misclassification error does not always have the highest prediction accuracy.Using classification rules as the explained variable,establish a Logistic model,the AIC value of the rough set is defined as the AIC value of the fitted model that the AIC criterion is provided to choice rough sets.The case analysis results show that the new algorithm can choice out rough set with high prediction accuracy.When there is small difference of misclassification error among many rough sets,new algorithm has bigger probability to choice out rough set with the highest prediction accuracy than misclassification criterion.
In practical application,when there is small difference of misclassification error among many rough sets constructed by multiple rough set construction algorithms,the rough set with least misclassification error does not always have the highest prediction accuracy.Using classification rules as the explained variable,establish a Logistic model,the AIC value of the rough set is defined as the AIC value of the fitted model that the AIC criterion is provided to choice rough sets.The case analysis results show that the new algorithm can choice out rough set with high prediction accuracy.When there is small difference of misclassification error among many rough sets,new algorithm has bigger probability to choice out rough set with the highest prediction accuracy than misclassification criterion.
引文
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[2]刘文军,赵利萍,肖旗梅.基于粗糙集与模糊集的信息检索算法[J].模糊系统与数学,2012,26(6):159~165.
[3]Kierczak M,et al.A rough set-based model of HIV-1reverse transcriptase resistome[J].Bioinformatics and Biology Insights,2009,3:109~127.
[4]Qian Y H,et al.An efficient accelerator for attribute reduction from incomplete data in rough set framework[J].Pattern Recognition,2011,44(8):1658~1670.
[5]钱进等.MapReduce框架下并行知识约简算法模型研究[J].计算机科学与探索,2013,7(1):35~45.
[6]Qian Y H,et al.Positive approximation:An accelerator for attribute reduction in rough set theory[J].Artificial Intelligence,2010,174(9~10):597~618.
[7]Dubois D,Prade H.Rough fuzzy sets and fuzzy rough sets[J].International Journal of General Systems,1990,17(2~3):191~209.
[8]Ziarko W.Variable precision rough set model[J].Journal of Computer and System Science,1993,46:39~59.
[9]Yao Y Y,Wong S K M,Lingras P.A decision-theoretic rough set model[J].Methodologies for Intelligent System,1990:17~25.
[10]Qian Y H,et al.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949~970.
[11]王国胤等.不确定信息的粗糙集表示和处理[J].重庆邮电大学学报(自然科学版),2010,22(5):541~544.
[12]Cornelis C,et al.Attribute selection with fuzzy decision reducts[J].Information Sciences,2010,180(2):209~224.
[13]范霄文.基于粗糙集的定性数据分析方法研究[D].厦门:厦门大学,2008.
[14]邓维斌,王国胤,胡峰.基于优势关系粗糙集的自主式学习模型[J].计算机学报,2014,37(12):2408~2418.
[15]翟育明,蔡红,郭斌.(α,β)集对限制优势粗糙集及决策模型[J].系统管理学报,2014,23(3):437~443.
[16]杨贵军,孟杰,王双喜.基于赤池信息准则的分类回归决策树剪枝算法[J].计算机应用,2014,34(S2):147~150.
[17]Akaike H.Autoregressive model fitting for control[J].Annals of the Institute of Statistical Mathematics,1971,23(1):163~180.