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

Survival Tree01:19

Survival Tree

166
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.
 Building a Survival Tree
Constructing a...
166
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.7K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.2K
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...
2.2K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

2.7K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
2.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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

Performance comparison between a deep learning model and spine surgeons in detecting cervical spinal cord compression on radiographs.

Journal of neurosurgery. Spine·2026
Same author

Development and Validation of an AI-Integrated System for Automated Fracture Detection and Pedicle Puncture Planning in Lumbar Osteoporotic Vertebral Compression Fractures Based on the Nine-Grid Area Division Method.

Orthopaedic surgery·2026
Same author

Biomechanical analysis of the interlaminar dynamic stabilization system (IntraSPINE) in unilateral biportal endoscopic discectomy for huge lumbar disc herniation: a finite element study.

BMC musculoskeletal disorders·2026
Same author

Technical Limitations and Implant Developments in Percutaneous Kyphoplasty for Osteoporotic Vertebral Compression Fractures.

Orthopaedic surgery·2026
Same author

Genetic evidence for causal effects of lifestyle factors, psychiatric factors, and socioeconomic status on various spinal disorders.

Postgraduate medical journal·2025
Same author

Modifiable risk factors for perioperative hidden blood loss in unilateral biportal endoscopic surgery: a systematic review and meta-analysis.

Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques·2025
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 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

7.6K

使用多策略改进的优化算法在软件缺陷预测中的功能选择.

Qi Fei1,2, Guisheng Yin1, Zhian Sun2

  • 1College of Computer Science and Technology, Harbin Engineering University, Harbin, China.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种增强的优化算法 (MEPO) 和二进制版本 (BMEPO),用于有效的软件缺陷预测和功能选择. 新型异质数据堆叠集体学习算法 (HEDSE) 显著提高了缺陷检测的准确性.

关键词:
没有平衡的数据集.机器学习是机器学习.的优化优化 的优化软件缺陷预测软件缺陷预测堆叠在一起的合奏.

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

7.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

相关实验视频

Last Updated: Sep 18, 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

7.6K
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

7.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 软件缺陷检测对于提高软件质量和降低成本至关重要.
  • 传统方法面临的挑战是无关紧要的功能和低于最佳的优化.
  • 现有的优化算法可能会遭受过早的融合和有限的全球搜索.

研究的目的:

  • 开发一种新的多策略增强的优化算法 (MEPO),解决原PO算法的局限性.
  • 为了在软件缺陷预测中有效选择功能,引入二进制MEPO (BMEPO).
  • 提出一个异质数据堆叠集体学习算法 (HEDSE),以提高缺陷预测性能.

主要方法:

  • 开发了一种多策略增强的优化算法 (MEPO).
  • 用二进制MEPO (BMEPO) 来优化特征选择的应用.
  • 实施一个异质数据堆叠集体学习算法 (HEDSE) 进行分类.

主要成果:

  • 在基准函数上,MEPO表现出卓越的融合速度和解决方案准确性.
  • 在特征选择质量和分类性能方面,BMEPO显示出更强的竞争力.
  • 在16个开源软件缺陷数据集上,HEDSE显著优于现有方法.

结论:

  • 拟议的MEPO,BMEPO和HEDSE为软件缺陷预测提供了一种新有效的解决方案.
  • 这些方法对于提高软件质量和降低成本具有显著的实际价值.
  • 该研究为优化现实世界软件工程中的缺陷检测提供了一个强大的框架.