基于用电特征单一视角数据的中小企业生命周期阶段识别
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摘要
以我国某地区黑色金属加工及冶炼行业的212家中小企业为样本,先使用带行业影响修正的用电量增长率法标注样本,再利用K-means聚类算法和SMOTE过采样技术提取数据集特征和平衡训练集类别,最后采用经MetaCost元代价敏感算法改造后的梯度提升决策树模型进行企业生命周期阶段的识别建模和预测。构建的七项特征具有典型的长尾性质,表现在对模型的预测能力贡献上它们的重要度比较平均一致。经过代价矩阵调参,模型对失衡类别企业样本的最佳查准率和查全率分别为83.3%和88.9%。通过与传统方法结果的横向Kappa一致性检验和纵向实证分析,验证了基于单视角企业用电数据利用机器学习算法模型来识别企业生命周期阶段的可信性和有效性。
Firstly,this paper adopts the power growth rate method with industry impact correction to label samples which are 212 small and medium-sized enterprises with respect to the ferrous metal smelting and rolling industry.Then it uses the K-means clustering and SMOTE oversampling techniques to extract data features and to balance the training set respectively.Finally,it adopts the gradient boosting decision tree model that is modified by MetaCost sensitive algorithm to recognize and predict those enterprises' life cycle phases.The seven constructed features have typical long tail characteristics,which show their importance to the model prediction ability is relatively uniform.By tuning the cost matrix parameter,precision and recall rates for imbalanced testing samples are respectively as high as 83.3% and 88.9%.At last,it confirms the credibility and the validity of machine learning algorithms that determine enterprises' life cycle phases based on single perspective electricity data by means of kappa consistency test and empirical analysis.
Firstly,this paper adopts the power growth rate method with industry impact correction to label samples which are 212 small and medium-sized enterprises with respect to the ferrous metal smelting and rolling industry.Then it uses the K-means clustering and SMOTE oversampling techniques to extract data features and to balance the training set respectively.Finally,it adopts the gradient boosting decision tree model that is modified by MetaCost sensitive algorithm to recognize and predict those enterprises' life cycle phases.The seven constructed features have typical long tail characteristics,which show their importance to the model prediction ability is relatively uniform.By tuning the cost matrix parameter,precision and recall rates for imbalanced testing samples are respectively as high as 83.3% and 88.9%.At last,it confirms the credibility and the validity of machine learning algorithms that determine enterprises' life cycle phases based on single perspective electricity data by means of kappa consistency test and empirical analysis.
引文
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[7]王炳成.企业生命周期研究述评[J].技术经济与管理研究,2011(4):52-55.
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[15]孙正林,王要武,刘红娜.规模经济:延长中小企业生命周期的理性选择[J].北京理工大学学报:社会科学版,2009,11(3):24-27.
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[18]衣长军,谢月婷.基于熵视角的企业生命周期阶段判定研究[J].科技管理研究,2013,33(14):224-229.
[19]赵蒲,孙爱英.资本结构与产业生命周期:基于中国上市公司的实证研究[J].管理工程学报,2005,19(3):42-46.
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[21]赵黎明,潘康宇.基于外部视角的企业生命周期判断方法[J].西安电子科技大学学报:社会科学版,2007,17(2):42-47.
[22]冯变英.基于kappa一致性检验的企业生命周期判定方法比较[J].数理统计与管理,2014,33(4):673-681.
[23]王震勤,杨孟秋.基于统计检验的企业生命周期判定方法比较[J].统计与决策,2017(18):174-177.
[24]刘同新,杨翠红,房勇等.基于用电特征单一视角数据的中小企业电费偿付能力风险识别和预测[J].技术经济,2018,37(2):91-96.
[25]申秦.基于生命周期的中小企业信用评价研究[D].江苏镇江:江苏大学,2011.
[26]HE H,GARCIA E A.Learning from Imbalanced Data[J].IEEE Transactions on Knowledge&Data Engineering,2009,21(9):1263-1284.
[27]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357.
[28]DOMINGOS P.MetaCost:a General Method for Making Classifiers Cost-sensitive[C].New York:ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining,1999.
[29]闫明松,周志华.代价敏感分类算法的实验比较[J].模式识别与人工智能,2005,18(5):628-635.
[30]吴琛越.成长阶段企业的定量分析---基于企业生命周期理论[J].华东经济管理,2008,22(6):63-67.
[2]KIMBERLY J R,MILES R H.The Organization Life Cycle[M].London:Jossey-Bass Publishers,1981.
[3]ADIZES I.Corporate Lifecycles:How and Why Corporations Grow and Die and What to Do about It[M].New York:Prentice Hall,1989.
[4]陈佳贵.关于企业生命周期与企业蜕变的探讨[J].中国工业经济,1995(11):5-13.
[5]李业.企业生命周期的修正模型及思考[J].南方经济,2000(2):47-50.
[6]周三多,邹统钎.战略管理思想史[M].上海:复旦大学出版社,2002.
[7]王炳成.企业生命周期研究述评[J].技术经济与管理研究,2011(4):52-55.
[8]JENKINS D S,KANE G D,VELURY U.The impact of the corporate life-cycle on the value-relevance of disaggregated earnings component[J].Review of Accounting&Finance,2004(3):5-20.
[9]王文亮,孟丽莎,冯军政.民营科技企业集群生命周期特点与战略选择[J].经济经纬,2005(3):116-119.
[10]郭际,李南,白奕欣.基于生命周期理论的企业危机管理动态分析[J].科学学与科学技术管理,2006,27(7):65-66.
[11]周连久,李志鹏.论企业生命周期与创新[J].技术经济,1997(5):38-40.
[12]王毅达.家族企业生命周期不同阶段的传承研究[J].技术经济,2010,29(10):126-129.
[13]单文,韩福荣.三维空间企业生命周期模型[J].北京工业大学学报,2002,28(1):117-120.
[14]李永峰,张明慧.论企业生命周期[J].太原理工大学学报:社会科学版,2004(3):21-25.
[15]孙正林,王要武,刘红娜.规模经济:延长中小企业生命周期的理性选择[J].北京理工大学学报:社会科学版,2009,11(3):24-27.
[16]KAZANJIAN R K,DRAZIN R.An Empirical Test of Stage of Growth Progression Model[J].Management Science,1989,35(12):1489-1503.
[17]熊义杰.企业生命周期分析方法研究[J].数理统计与管理,2002,21(3):36-39.
[18]衣长军,谢月婷.基于熵视角的企业生命周期阶段判定研究[J].科技管理研究,2013,33(14):224-229.
[19]赵蒲,孙爱英.资本结构与产业生命周期:基于中国上市公司的实证研究[J].管理工程学报,2005,19(3):42-46.
[20]曹裕,陈晓红,王傅强.我国上市公司生命周期划分方法实证比较研究[J].系统管理学报,2010,19(3):313-322.
[21]赵黎明,潘康宇.基于外部视角的企业生命周期判断方法[J].西安电子科技大学学报:社会科学版,2007,17(2):42-47.
[22]冯变英.基于kappa一致性检验的企业生命周期判定方法比较[J].数理统计与管理,2014,33(4):673-681.
[23]王震勤,杨孟秋.基于统计检验的企业生命周期判定方法比较[J].统计与决策,2017(18):174-177.
[24]刘同新,杨翠红,房勇等.基于用电特征单一视角数据的中小企业电费偿付能力风险识别和预测[J].技术经济,2018,37(2):91-96.
[25]申秦.基于生命周期的中小企业信用评价研究[D].江苏镇江:江苏大学,2011.
[26]HE H,GARCIA E A.Learning from Imbalanced Data[J].IEEE Transactions on Knowledge&Data Engineering,2009,21(9):1263-1284.
[27]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357.
[28]DOMINGOS P.MetaCost:a General Method for Making Classifiers Cost-sensitive[C].New York:ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining,1999.
[29]闫明松,周志华.代价敏感分类算法的实验比较[J].模式识别与人工智能,2005,18(5):628-635.
[30]吴琛越.成长阶段企业的定量分析---基于企业生命周期理论[J].华东经济管理,2008,22(6):63-67.