Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

What Are Outliers?01:12

What Are Outliers?

5.1K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
5.1K
Unusual Results01:16

Unusual Results

3.8K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
3.8K
Outliers and Influential Points01:08

Outliers and Influential Points

6.3K
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...
6.3K
z Scores and Unusual Values01:07

z Scores and Unusual Values

11.0K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
11.0K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.1K
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...
4.1K
Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

3.3K
Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
3.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction.

Transactions on machine learning research·2026
Same author

Hierarchical Active Learning with Label Proportions on Data Regions.

IEEE transactions on knowledge and data engineering·2025
Same author

Augmentation-Free Contrastive Learning for EKG Classification.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )·2025
Same author

Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )·2024
Same author

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds.

Scientific reports·2024
Same author

Personalized event prediction for Electronic Health Records.

Artificial intelligence in medicine·2023
Same journal

Active Learning of Multi-Class Classifiers with Auxiliary Probabilistic Information.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2019
Same journal

Online Conditional Outlier Detection in Nonstationary Time Series.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2018
Same journal

A Multi-Label Classification Approach for Coding Cancer Information Service Chat Transcripts.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2017
Same journal

Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2017
Same journal

Group-Based Active Learning of Classification Models.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2017
Same journal

Examining Healthcare Utilization Patterns of Elderly Middle-Aged Adults in the United States.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium·2016
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data.

Charmgil Hong1, Milos Hauskrecht1

  • 1Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260.

Proceedings of the ... International Florida AI Research Society Conference. Florida AI Research Symposium
|February 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for multivariate conditional outlier detection, identifying unusual input-output patterns in data. The method uses a weighted probabilistic model, offering improved accuracy in anomaly detection across diverse datasets.

More Related Videos

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Related Experiment Videos

Last Updated: Jan 29, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Area of Science:

  • Data Science
  • Machine Learning
  • Statistics

Background:

  • Conditional outlier detection is crucial for identifying anomalies in data with contextual dependencies.
  • Multivariate conditional outlier detection specifically addresses complex datasets with continuous inputs and binary outputs.

Purpose of the Study:

  • To develop a novel framework for multivariate conditional outlier detection.
  • To identify abnormal associations between continuous input (context) and binary output (response) vectors.
  • To enhance outlier detection accuracy by weighting model components based on reliability.

Main Methods:

  • A decomposable conditional probabilistic model is proposed for identifying abnormal input-output associations.
  • Component weights are calculated using global (all data) and local (neighboring instances) approaches.
  • The framework combines model components using weights reflecting their reliability in outlier assessment.

Main Results:

  • Experimental results demonstrate the framework's effectiveness in identifying multivariate conditional outliers.
  • The proposed weighting methods (global and local) contribute to successful anomaly detection.
  • The framework shows robust performance across data from various domains.

Conclusions:

  • The novel framework effectively addresses multivariate conditional outlier detection.
  • Weighted probabilistic models offer a promising approach for complex anomaly detection tasks.
  • The method's adaptability across different data domains highlights its practical utility.