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相关概念视频

Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Upsampling01:22

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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相关实验视频

Updated: Jun 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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注意:用于可解释的信用评分的非参数过量抽样技术.

Seongil Han1, Haemin Jung2, Paul D Yoo3

  • 1School of Computing & Mathematical Sciences, University of London, Birkbeck College, London, UK.

Scientific reports
|October 31, 2024
PubMed
概括
此摘要是机器生成的。

一种新方法,即可解释信用评分的非参数过量采样技术 (NOTE),可以在不平衡的数据集上提高信用评分的准确性. 它提高了模型的稳定性和可解释性,优于金融风险评估的现有过量抽样技术.

关键词:
有条件的瓦斯斯坦生成对抗网络.信用评分是指信用评分.可解释的人工智能不平衡的阶级是不平衡的.过量采样过度采样堆叠的自动编码器

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Last Updated: Jun 9, 2025

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科学领域:

  • 计算金融是指计算金融.
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 信用评分模型对于金融机构管理借款人风险和确保利至关重要.
  • 机器学习提高了信用评分的准确性,但不平衡的数据集和非线性数据带来了重大挑战.
  • 现有的方法,如合成少数群体过量采样技术 (SMOTE),难以处理高维,非线性数据,并可能引入噪声.

研究的目的:

  • 为了解决不平衡的信用评分数据集当前超标采样技术的局限性.
  • 开发一种新的方法来提取非线性潜伏特征并提高模型可解释性.
  • 引入可解释信用评分 (NOTE) 的非参数过量采样技术作为一个优质的替代方案.

主要方法:

  • 开发了可解释信用评分 (NOTE) 的非参数超标采样技术,一种统一的方法.
  • 集成了一个非参数堆叠自动编码器 (NSA) 来捕获非线性潜伏特征.
  • 利用条件瓦瑟斯坦GANs (cWGANs) 进行少数阶级过量抽样,并纳入了以可解释性为重点的分类过程.

主要成果:

  • 与最先进的过量采样技术相比,NOTE方法显示出更高的性能.
  • 注:在非线性和不平衡的信用评分数据集上显著提高了分类准确性和模型稳定性.
  • 提出的技术提高了信用评分模型结果的解释性.

结论:

  • NOTE方法有效地处理复杂,不平衡的信用评分数据.
  • NOTE为提高信用评分模型的预测能力和可解释性提供了一个有希望的解决方案.
  • 这项研究通过提供更强大,更透明的方法来推进机器学习在金融风险评估中的应用.