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Uncertainty: Overview

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.

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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...

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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...

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Related Experiment Video

Updated: Oct 13, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator.

Jifeng Guo¹, Zhiqi Pang¹, Wenbo Sun¹

¹College of Information, Northeast Forestry University, Harbin 150040, China.

Computational Intelligence and Neuroscience

|November 15, 2021

Summary

This summary is machine-generated.

This study introduces a novel Redundancy Removal Adversarial Active Learning (RRAAL) method. RRAAL enhances sample selection by considering distribution, uncertainty, and redundancy, outperforming existing active learning techniques.

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Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

Active learning strategies are crucial for efficient model training by selecting informative unlabeled data.
Existing methods often struggle with sample redundancy and accurate uncertainty estimation.

Purpose of the Study:

To propose a Redundancy Removal Adversarial Active Learning (RRAAL) method.
To improve the selection of valuable unlabeled samples for annotation by addressing distribution, uncertainty, and redundancy.

Main Methods:

Developed a RRAAL framework incorporating a representation generator, state discriminator, and redundancy removal module (RRM).
Introduced a norm online uncertainty indicator (Norm-OUI) for precise uncertainty scoring.
Implemented a greedy algorithm within the RRM to minimize redundant samples in the labeled pool.

Main Results:

The state discriminator, Norm-OUI, and RRM modules demonstrably improved RRAAL's performance.
RRAAL achieved superior results compared to state-of-the-art active learning methods across four datasets.

Conclusions:

The proposed RRAAL method effectively enhances active learning by integrating distribution, uncertainty, and redundancy considerations.
The novel components, including Norm-OUI and RRM, contribute significantly to improved model performance and efficiency.