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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Survival Tree01:19

Survival Tree

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 survival tree begins...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all points...

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Related Experiment Video

Updated: May 25, 2026

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

Uncertainty-aware instance-wise feature selection with adaptive graph regularization.

G Kirubavathi1, K J Vijayavarsini2, G S Vruthula Shruthi2

  • 1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India. g_kirubavathi@cb.amrita.edu.

Scientific Reports
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Uncertainty-aware Deep Instance-wise Feature Selection (UDIFS), a novel method that quantifies feature selection uncertainty. UDIFS achieves the lowest uncertainty while maintaining accuracy, offering more reliable feature relevance interpretations.

Keywords:
Adaptive graph learningConcrete distributionInstance-wise feature selectionInterpretabilityMonte Carlo dropoutUncertainty estimationexplainability

Related Experiment Videos

Last Updated: May 25, 2026

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

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Instance-wise feature selection aims to identify relevant attributes for individual samples, crucial for adaptive learning with high-dimensional data.
  • Current methods lack reliability by treating feature masks as definite, problematic in critical applications due to data imperfections and small sample sizes.
  • Existing approaches fail to estimate the reliability of selected features, leading to potentially flawed interpretations in sensitive domains.

Purpose of the Study:

  • To propose the first method, UDIFS, that addresses uncertainty inherent in the feature selection process itself, not just model predictions.
  • To develop a novel approach for estimating the reliability of feature relevance for individual instances.
  • To enable more trustworthy and interpretable feature selection in critical applications.

Main Methods:

  • UDIFS employs a stochastic process by learning a distribution over feature masks using a Concrete (Gumbel-Sigmoid) distribution.
  • Monte Carlo estimation via sampling multiple masks at inference time is used to quantify uncertainty in predictions and feature selection.
  • An adaptive Graph Laplacian regularization is applied directly to sampled masks to ensure consistency for semantically similar instances.

Main Results:

  • UDIFS (Graph) demonstrated the lowest feature selection uncertainty across diverse datasets (Car Evaluation, Dexter, IMDB Sentiment) compared to eight baseline methods.
  • The method maintained classification accuracy while significantly reducing uncertainty, outperforming existing instance-wise and neural feature selection techniques.
  • A positive uncertainty gap between correctly and incorrectly classified instances validates the semantic meaning of the learned uncertainty signal.

Conclusions:

  • UDIFS is the first approach to integrate uncertainty estimation directly into the feature selection phase and apply Graph Laplacian regularization to feature masks.
  • The findings suggest a paradigm shift towards evaluating explanations using both accuracy and reliability measures.
  • The developed uncertainty signal provides a more robust foundation for decision-making in critical applications, enhancing trust and interpretability.