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

Sampling Distribution01:12

Sampling Distribution

12.4K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.4K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
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...
1.6K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K

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

Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway.

Journal of integrative bioinformatics·2023
Same author

Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques.

Sensors (Basel, Switzerland)·2021
Same author

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

Bio Systems·2017
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

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

706

KCO:在即时软件缺陷预测中平衡类分布,使用内核交叉超采样.

Ahmad Muhaimin Ismail1,2, Siti Hafizah Ab Hamid1, Asmiza Abdul Sani1

  • 1Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.

PloS one
|April 11, 2024
PubMed
概括
此摘要是机器生成的。

内核交叉过量采样 (KCO) 通过创建多样化的数据集来改善缺陷预测. 这种新的过量采样技术可以减少噪音和冗余,从而更准确地发现软件缺陷.

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

7.5K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

相关实验视频

Last Updated: Jun 28, 2025

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

706
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

7.5K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 缺陷预测模型对于软件质量至关重要.
  • 不平衡的数据集对模型性能构成挑战.
  • 现有的重新采样方法往往无法解决数据冗余和噪声问题.

研究的目的:

  • 为了引入内核交叉超采样 (KCO),一种新的超采样技术.
  • 通过生成多样化和平衡的数据集来增强缺陷预测.
  • 为了减轻不平衡数据集中的冗余和噪声.

主要方法:

  • 核心主要组件分析 (KPCA) 用于缩小维度.
  • 频谱聚类用于识别最佳的插值区域.
  • 交叉插值以生成合成缺陷数据.

主要成果:

  • 在六个数据集中,KCO始终实现了21%至63%之间的F分数.
  • 与基线技术相比,KCO表现出优异的预测性能.
  • 在项目内部和跨项目缺陷预测中都观察到显著的改进.

结论:

  • KCO有效地解决了数据不平衡,冗余和缺陷预测中的噪声.
  • 拟议的技术提高了缺陷预测模型的准确性和可靠性.
  • KCO提供了一种有前途的方法来提高软件开发质量.