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相关概念视频

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Updated: Jul 5, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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带有不平衡数据的二进制分类

Jyun-You Chiang1, Yuhlong Lio2, Chien-Ya Hsu3

  • 1School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.

Entropy (Basel, Switzerland)
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个期望最大化 (EM) 算法,用于零膨胀的伯努利 (ZIBer) 模型,数据不平衡. 对于这些数据集,LightGBM和ZIBer模型显示了与人工神经网络 (ANN) 相比具有竞争力的预测性能.

关键词:
Entropy Entropy人工神经网络的人工神经网络预期最大化算法是指期望最大化算法.逻辑回归的逻辑回归零膨胀模型的零膨胀模型

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 计算统计学 计算统计学

背景情况:

  • 不平衡的数据,其特点是响应变量中过多的零计数,对二进制分类任务构成重大挑战.
  • 当前的方法在处理零膨胀和不平衡数据集时,难以准确地估计和预测参数.

研究的目的:

  • 提出一种预期最大化 (EM) 算法,以简化对零膨胀的伯努利 (ZIBer) 模型参数与不平衡数据的最大概率估计器 (MLEs) 的计算.
  • 使用蒙特卡洛模拟,将ZIBer模型的预测性能与流行的机器学习算法 (如LightGBM和人工神经网络 (ANN)) 进行比较.

主要方法:

  • 开发一个预期最大化 (EM) 算法,以有效地推导ZIBer模型参数的MLEs.
  • 实施物流回归模型,在ZIBer框架内将伯努利概率与共变量联系起来.
  • 使用蒙特卡洛模拟进行比较分析,以评估ZIBer,LightGBM和ANN模型的预测性能.

主要成果:

  • 没有任何一种方法在不平衡数据上的预测性表现的所有场景中表现出一致的主导地位.
  • 与人工神经网络 (ANN) 模型相比,零膨胀的伯努利 (ZIBer) 模型和LightGBM表现出更具竞争力的预测能力.
  • 拟议的EM算法有效地简化了对具有不平衡数据的ZIBer模型的参数估计.

结论:

  • 对于零膨胀不平衡数据集,ZIBer模型和LightGBM提供了强大的预测性能,在某些情况下超过了ANN.
  • 选择不平衡二进制分类模型时,应考虑数据的特定特征,因为没有普遍的最佳方法.
  • 开发的EM算法为ZIBer模型中的参数估计提供了一种高效的计算方法,特别有利于不平衡数据场景.