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Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

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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...
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What Are Outliers?01:12

What Are Outliers?

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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...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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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...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Related Experiment Video

Updated: Oct 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

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Toward Scalable and Unified Example-Based Explanation and Outlier Detection.

Penny Chong, Ngai-Man Cheung, Yuval Elovici

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 18, 2021
    PubMed
    Summary

    This study introduces prototype-based neural networks that offer example-based explanations for predictions and identify outliers. These networks improve understanding and verification in high-stakes AI decision-making.

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    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Explainable AI (XAI)

    Background:

    • High-stakes AI decisions require transparent explanations and outlier detection.
    • Current methods often treat explainability and outlier detection separately.

    Purpose of the Study:

    • To unify explainability and outlier detection in neural networks.
    • To propose and evaluate prototype-based student networks for example-based explanations and outlier identification.

    Main Methods:

    • Developed novel iterative prototype replacement algorithm for sampling training data.
    • Utilized prototype similarity scores for outlier detection.
    • Compared prototype-based networks against baseline models.

    Main Results:

    • Prototype-based networks provide meaningful example-based explanations.
    • Achieved promising outlier detection results.
    • Maintained classification accuracy without compromise.

    Conclusions:

    • Prototype-based networks offer a unified approach to explainability and outlier detection.
    • These networks enhance trust and verification in AI systems.
    • The proposed method demonstrates effectiveness beyond standard similarity kernels.