Data Mining from Large Data Set by Generalized Tree Regression Model

广义树回归模型从大数据集中进行数据挖掘

• 批准号: 11680437
• 负责人: SEKI Yoichi
• 金额: $ 2.11万
• 依托单位: Gunma University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1999
• 资助国家: 日本
• 起止时间: 1999 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11680437/
• 关键词: Tree regression model; Data mining; Minimum Description Length; Interaction effect; Linear regression; Poisson compound distribution; Care needs certification; Long-term care insurance; サービス量分布; 分岐規準

Our research project dealt with tree regression models as data mining method. We studied the below subjects as generalization of the ordinal tree regression model.(a) Generalization of the covariates use as additive termWe propose the method to estimate tree models which have linear regression terms in each node of regression tree, using degree of interaction effect as splitting criterion. The method enable us to make a simple tree which have fewer splits than ordinal tree regression models.(b) Generalization of distribution assumption of response variableOrdinal tree regression model assume implicitly that response variable distributes as normal distribution. To apply the models to the service time response data, we propose the method under the assumption that response variable has Poisson compound exponential distribution.(c) Varidity study of the tree regression modelWe analyze empirical data to confirm the varidity of proposed methods, for example, Long-term care service time data which was investigated for the Care Needs Certification in Japanese Long-term Care Insurance.The reduction of calculation time remains as future study.

我们的研究项目涉及树回归模型作为数据挖掘方法。我们研究了以下主题作为序数树回归模型的推广。(a)协变量作为加性项的泛化我们提出了以相互作用程度作为分裂准则，估计回归树的每个节点中具有线性回归项的树模型的方法。该方法使我们能够制作一个比有序树回归模型具有更少分裂的简单树。(b)响应变量分布假设的概化有序树回归模型隐含地假设响应变量呈正态分布。为了将模型应用于服务时间响应数据，我们提出了在响应变量具有泊松复合指数分布的假设下的方法。(c)树回归模型的可变性研究我们通过对实证数据的分析来验证所提出方法的可变性，例如日本长期护理保险的护理需求认证所调查的长期护理服务时间数据。计算时间的缩短仍有待进一步研究。

项目成果

宮野尚哉,筒井孝子,関庸一,谷口仁志: "適応型局所線形近似手法の要介護認定への応用"電子情報通信学会技術研究報告. CAS2000-49 NLP2000-57. 19-25 (2000)

Naoya Miyano、Takako Tsutsui、Yoichi Seki、Hitoshi Taniguchi：“自适应局部线性近似方法在护理认证中的应用” IEICE 技术研究报告 CAS2000-49 NLP2000-57 (2000)。

宮野尚哉,筒井孝子,関庸一,谷口仁志: "適応型局所線形近似手法の要介護認定への応用"電子情報通信学会技術研究報告. CAS2000-49NLP2000-57. 19-25 (2000)

Naoya Miyano、Takako Tsutsui、Yoichi Seki、Hitoshi Taniguchi：“自适应局部线性近似方法在护理认证中的应用” IEICE 技术研究报告。 CAS2000-49NLP2000-57。

関庸一,筒井孝子,宮野尚哉: "要介護認定一次判定方式の基礎となった統計モデルの妥当性"応用統計学. 29・2. 101-110 (2000)

Yoichi Seki、Takako Tsutsui、Naoya Miyano：“构成确定长期护理需求的主要确定方法基础的统计模型的有效性”，《应用统计学》29・2（2000 年）。

Yoichi Seki, Takako Tsutsui and Takaya Miyano: "Varidity of Statistical Model for Primary Decision of Care Needs Certification"Japanese Journal of Applied Statistics. Vol. 29, No. 2. 101-110 (2000)

Yoichi Seki、Takako Tsutsui 和 Takaya Miyano：“护理需求认证初级决策的统计模型的多样性”日本应用统计学杂志。

Takaya Miyano, Takako Tsutsui, Yoichi Seki and Hitoshi Taniguchi: "Application of Adaptive Local Nonlinear approximation to the Certification of Long-term care"Technical Report of IEICE. CAS2000-49, NLP2000-57. 19-25 (2000)

Takaya Miyano、Takako Tsutsui、Yoichi Seki 和 Hitoshi Taniguchi：“自适应局部非线性近似在长期护理认证中的应用”IEICE 技术报告。