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相关概念视频

Weighted Mean00:57

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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一个通用的自适应无监督的功能选择与自动权重.

Huming Liao1, Hongmei Chen1, Tengyu Yin1

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, 611756, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu 611756, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, China.

Neural networks : the official journal of the International Neural Network Society
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概括
此摘要是机器生成的。

本研究引入了一种新的无监督特征选择 (UFS) 方法,GAWFS,它有效地识别了用于集群的歧视性特征,而不会改变原始数据结构. 与现有的UFS技术相比,GAWFS在处理高维数据方面表现出卓越的性能.

关键词:
适应式图形学习功能权重的权重是特征权重.非负矩阵因子化的因子化没有监督的特征选择选择.

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科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 人工智能的人工智能

背景情况:

  • 高维数据在效率和可靠性方面带来了挑战.
  • 无监督特征选择 (UFS) 由于数据标签的成本至关重要.
  • 现有的嵌入式UFS方法经常在控制稀疏性和保留特征结构方面扎.

研究的目的:

  • 提出一种新的无监督特征选择模型,GAWFS.
  • 解决现有的UFS方法的局限性,特别是那些使用稀疏投影矩阵的方法.
  • 识别可以增强数据集群的特征,而不会改变原来的特征空间.

主要方法:

  • 开发了一种具有自动权重的通用自适应无监督特征选择 (GAWFS) 模型.
  • 使用非负矩阵分解和自适应图表学习.
  • 使用特征权重矩阵 (Θ) 来识别歧视性特征并执行特征选择.

主要成果:

  • GAWFS有效地识别了聚类的歧视性特征.
  • 该方法避免将数据投射到一个低维的嵌入空间中,从而保留原始特征结构.
  • 实验结果表明,GAWFS在合成和现实数据集上胜过了几种最先进的UFS方法.

结论:

  • GAWFS为无监督的特征选择提供了一种优越的方法.
  • 该模型的自动权重机制提供了有效的功能过.
  • GAWFS是一种有前途的技术,用于高效可靠的高维数据分析.