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Extracting fuzzy measures from sample data: Optimization algorithms and applications.

机译：从样本数据中提取模糊度量：优化算法和应用。

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摘要

In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conflicting and non-homogenously satisfied. We use non-additive (fuzzy) measures combined with the Choquet integral to solve MCDM problems, for they allow to model and aggregate the levels of satisfaction of the several criteria of the problem at hand by considering the relationship between these criteria. In practice, it is difficult to identify such fuzzy measures. An automated process is then necessary and can be used when sample data is available. Several optimization approaches have been used to extract fuzzy measures from sample data, for example, genetic algorithms, gradient descent algorithms, and so on. We propose to automatically model experts' decision process and extract the fuzzy measure corresponding to their reasoning process; to do this, we use samples of the target experts' decision as seed data to automatically extract the fuzzy measure corresponding to the experts' decision process. In particular, we propose several approaches to extract fuzzy measures from sample data, including the Bees algorithm, an adaptive hybrid algorithm that combines the Bees algorithm and an interval constraint solver, and a speculative algorithm. We also apply the proposed approaches to the real data to show the applicability of the algorithms. The real applications include software quality assessment (SQA) and student risk level prediction. Our experimental results show that we are able to improve some of the results obtained through previous approaches, e.g., through machine learning techniques for software quality assessment problem and Cox proportional hazards (PH) regression model for student risk prediction problem.
机译：在多标准决策（MCDM）中，决策基于通常冲突且非同质满足的几个标准。我们将非加性（模糊）度量与Choquet积分结合使用来解决MCDM问题，因为它们允许通过考虑这些标准之间的关系来建模和汇总手头几个问题的满意度。在实践中，很难识别这种模糊度量。这样就需要一个自动化过程，并且在有样本数据时可以使用该过程。几种优化方法已用于从样本数据中提取模糊度量，例如遗传算法，梯度下降算法等。我们建议对专家的决策过程进行自动建模，并提取与其推理过程相对应的模糊度量；为此，我们将目标专家决策的样本用作种子数据，以自动提取与专家决策过程相对应的模糊度量。特别是，我们提出了几种从样本数据中提取模糊度量的方法，包括Bees算法，结合Bees算法和区间约束求解器的自适应混合算法以及一种推测算法。我们还将提出的方法应用于实际数据，以显示算法的适用性。实际的应用程序包括软件质量评估（SQA）和学生风险水平预测。我们的实验结果表明，我们能够改进通过先前方法获得的一些结果，例如，通过针对软件质量评估问题的机器学习技术以及针对学生风险预测问题的Cox比例风险（PH）回归模型。

著录项

  • 作者

    Wang, Xiaojing.;

  • 作者单位

    The University of Texas at El Paso.;

  • 授予单位 The University of Texas at El Paso.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 语言学;
  • 关键词

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