Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Surveys02:16

Surveys

14.7K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
14.7K
Causality in Epidemiology01:21

Causality in Epidemiology

157
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
157
Correlation and Causation01:27

Correlation and Causation

37.2K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
37.2K
Data Collection by Survey01:07

Data Collection by Survey

6.3K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
6.3K
Types of Surveys01:27

Types of Surveys

25
Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
25
Cause and Effect01:53

Cause and Effect

10.8K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

From One Domain to Another: The Pitfalls of Gender Recognition in Unseen Environments.

Sensors (Basel, Switzerland)·2025
Same author

Adaptive Spatial Transformation Networks for Periocular Recognition.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

因果关系,机器学习和特征选择:一项调查

Asmae Lamsaf1, Rui Carrilho1, João C Neves2

  • 1IT: Instituto de Telecomunicações, University of Beira Interior, 6200-001 Covilhã, Portugal.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

本文回顾了因果发现和因果推理方法. 将因果关系整合到机器学习中,可以增强功能选择,以便在复杂系统中进行可靠的决策.

关键词:
因果发现的发现.有关因果推理的推理.有关因果关系的因果关系功能提取 特性提取功能选择 功能选择机器学习是机器学习.传感器数据 传感器数据

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599

相关实验视频

Last Updated: May 10, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 因果关系研究 研究因果关系

背景情况:

  • 了解因果关系对于复杂的数据分析至关重要.
  • 传统方法通常依赖于相关性,可能缺少重要的因果关系.
  • 因果关系是提高机器学习模型稳定性和准确性的关键.

研究的目的:

  • 审查因果发现和因果推断中的方法.
  • 突出机器学习的特征选择中的因果关系的应用.
  • 展示因果推理如何改善复杂系统中的决策.

主要方法:

  • 对因果发现技术的审查,以图形表示变量影响.
  • 对量化变量影响的因果推理方法的审查.
  • 探索因果关系驱动的特征选择,特别是传感器数据.

主要成果:

  • 因果推理提高了机器学习模型在预测和分类中的性能.
  • 基于因果关系的特征选择识别了关联方法错过的关键链接.
  • 改进的功能选择支持诸如故障和异常检测等应用程序.

结论:

  • 整合因果发现和推断可以加强机器学习模型.
  • 由因果关系驱动的特征选择导致更有洞察力和可操作的结果.
  • 这种方法可以在关键系统的维护和分析中进行更好的决策.