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Comparing the performance of ordinal logistic regression and artificial neural network when analyzing ordinal data.

机译：分析顺序数据时比较顺序逻辑回归和人工神经网络的性能。

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摘要

The purpose of this study is to compare the performance of the Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN) models when analyzing ordinal data using different scenarios by varying the combinations of the marginal probability distributions and correlation coefficients. Two internal links in the Service Profit Chain (SPC), the relationship between employee perceived value of the internal and external determinants of employee satisfaction and employee overall satisfaction and the relationship between employee overall satisfaction and job performance are used as a framework to build the OLR and ANN models. Ordinal data collected from surveys at two training restaurants (Taylors' Dining at Oklahoma State University, USA and Fajar Teaching Restaurant at Universitas Negeri Malang, Indonesia) and simulated correlated ordinal data are fitted to the OLR and ANN models in order to compare the mean of misclassification rates from each model. A model with a lower misclassification rate is preferred.;The application of the OLR and ANN models to analyze a causal relationship between one input variable and one output variable results in no statistically significant difference between the means of the misclassification rates resulting from both models for all three scenarios tested. On the other hand, the application of the OLR and ANN models to analyze a causal relationship between three input variables and one output variable results in a statistically significant difference between the means of the misclassification rates resulting from both models for all three scenarios tested. The OLR model outperforms the ANN model when it is used to analyze ordinal data that has similar marginal probabilities and correlation coefficients to Taylors' data. In contrast, the ANN model outperforms the OLR model when it is used to analyze ordinal data that has marginal probabilities and correlation coefficients either similar to FTR's data or randomly distributed. These results suggest that the complexity of the problem, which is represented by the number of input variables (attributes), and the complexity of the data structures, which is represented by the correlation coefficient and marginal probability distribution including the kurtosis, should be considered before fitting data sets to either the OLR or ANN models.
机译：本研究的目的是通过改变边际概率分布和相关系数的组合，比较在使用不同场景分析序数数据时序数逻辑回归（OLR）模型和人工神经网络（ANN）模型的性能。服务利润链（SPC）中的两个内部链接，员工满意度的内部和外部决定因素的员工感知价值与员工总体满意度之间的关系以及员工总体满意度与工作绩效之间的关系被用作构建OLR的框架和ANN模型。从两个培训餐厅（美国俄克拉何马州立大学的泰勒餐厅和印度尼西亚尼日利玛琅大学的Fajar教学餐厅）的调查中收集的序数数据和OLR和ANN模型拟合了模拟的相关序数数据，以便比较每个模型的误分类率。最好使用错误分类率较低的模型。使用OLR和ANN模型分析一个输入变量和一个输出变量之间的因果关系时，两种模型所导致的错误分类率均值之间在统计上没有显着差异这三个场景都经过测试。另一方面，使用OLR和ANN模型分析三个输入变量和一个输出变量之间的因果关系会导致在两种情况下测试的两种模型的两种模型导致的误分类率的均值在统计上存在显着差异。当OLR模型用于分析序数数据时，其性能优于ANN模型，序数数据的边际概率和相关系数与Taylors的数据相似。相反，当ANN模型用于分析具有边际概率和相关系数（类似于FTR数据或随机分布）的序数数据时，其性能优于OLR模型。这些结果表明，应先考虑问题的复杂性（由输入变量（属性）的数量表示）和数据结构的复杂性（由相关系数和包括峰度的边际概率分布表示），将数据集拟合到OLR或ANN模型。

著录项

  • 作者

    Larasati, Aisyah.;

  • 作者单位

    Oklahoma State University.;

  • 授予单位 Oklahoma State University.;
  • 学科 Sociology Theory and Methods.;Engineering Industrial.;Statistics.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 197 p.
  • 总页数 197
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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