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

Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
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...
4.0K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
What is Central Tendency?01:14

What is Central Tendency?

14.6K
Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large quantities of data and developing an inference, one needs to identify a value representative of the entire data set. Characteristics such as central tendency, extreme values, range of measurements, or the most repeated value can help better understand the data.
The central tendency is the most conventionally used data characteristic. It is a...
14.6K
Review and Preview01:13

Review and Preview

8.9K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
8.9K
Data: Types and Distribution01:19

Data: Types and Distribution

717
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
717
Data Collection by Observations01:08

Data Collection by Observations

11.9K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
11.9K

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

Exploiting Data Distribution: A Multi-Ranking Approach.

Entropy (Basel, Switzerland)·2025
Same author

Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles.

Entropy (Basel, Switzerland)·2025
Same author

Selected Data Mining Tools for Data Analysis in Distributed Environment.

Entropy (Basel, Switzerland)·2023
Same author

Improved EAV-Based Algorithm for Decision Rules Construction.

Entropy (Basel, Switzerland)·2023
Same author

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features.

Entropy (Basel, Switzerland)·2022
Same author

Decision Rules Derived from Optimal Decision Trees with Hypotheses.

Entropy (Basel, Switzerland)·2021

相关实验视频

Updated: Jun 25, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

特征特征的重要性及其形式对数据探索的重要性.

Urszula Stańczyk1, Beata Zielosko2, Grzegorz Baron1

  • 1Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland.

Entropy (Basel, Switzerland)
|May 24, 2024
PubMed
概括

功能相关性和数据分类显著影响知识发现. 这项研究表明,在属性排名的指导下,逐渐的分离化提高了作者归因任务中的预测准确性.

关键词:
属性域名属性域名离散的离散化 离散化模式识别 模式识别排名 排名 排名 排名 排名相关性 相关性这是一种造型计量法,一种造型计量法.

更多相关视频

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.1K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

相关实验视频

Last Updated: Jun 25, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.1K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

科学领域:

  • 计算机科学 计算机科学
  • 数据挖掘 数据挖掘
  • 机器学习 机器学习

背景情况:

  • 输入特征特征极大地影响知识发现工具和方法的选择和性能.
  • 变量类型,域和它们的相关性会影响数据探索的有效性,并且可能需要预处理.
  • 像排名一样,特征选择和减少技术对于估计属性的重要性至关重要.

研究的目的:

  • 调查特征相关性和数据分类对知识发现绩效的影响.
  • 提出和评估一种由属性排名控制的逐步分离的程序.
  • 评估这种方法在书法测量和作者归因领域的有效性.

主要方法:

  • 雇员监督和无监督的秘密化方法.
  • 使用属性排名进行受控,逐步的离散.
  • 将这些方法应用于用于二进制作者归因的样度域的数据集.
  • 通过选择的分类器进行了广泛的测试.

主要成果:

  • 通过逐步分离实现了基于相关性的数据表格条件化.
  • 在许多情况下,部分离散的数据集显示出更高的预测准确性.
  • 根据属性排名指导的拟议的离散程序被证明是有效的.

结论:

  • 特性相关性和适当的数据转换,如指导离散,是提高机器学习模型性能的关键.
  • 拟议的方法提供了一种可行的方法来提高作者归因的准确性.
  • 了解和操纵特征特征对于成功的知识发现至关重要.