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00:59Uncertainty: Overview
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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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01:10Propagation of Uncertainty from Systematic Error
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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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00:54Uncertainty: Confidence Intervals
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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...
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00:59Propagation of Uncertainty from Random Error
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03:37Uncertainty in Measurement: Accuracy and Precision
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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01:31Classification of Systems-II
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback.
Paulo Vitor de Campos Souza1, Edwin Lughofer1
1Department of Knowledge-Based Mathematical Systems, Johannes Kepler Universitat Linz, Science Park 2 (6th Floor), Altenbergerstrasse 69, 4040 Linz, Austria.
Evolving Systems
|April 3, 2023
Summary
Integrating expert labeling uncertainty into evolving fuzzy neural classifiers (EFNC-U) improves accuracy. This approach enhances model interpretability and robustness, even with up to 20% uncertainty in data labels.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Data Mining
Background:
- Data quality significantly impacts model performance.
- Labeling uncertainty in data can arise from expert inexperience or low confidence.
- Evolving fuzzy neural networks (EFNC) are powerful tools for complex problem-solving.
Purpose of the Study:
- To introduce EFNC-U, an approach integrating expert labeling uncertainty into EFNC.
- To enhance the interpretability of fuzzy classification rules.
- To improve the accuracy and robustness of EFNC models when dealing with uncertain data.
Main Methods:
- Proposed EFNC-U by incorporating expert input on labeling uncertainty.
- Conducted binary pattern classification tests in cyber invasion and auction fraud detection scenarios.
- Evaluated model accuracy and rule interpretability with varying levels of simulated uncertainty.
Main Results:
- EFNC-U demonstrated improved accuracy trends compared to models trained on uncertain data without explicit uncertainty consideration.
- The approach showed robustness, with accuracy trends similar to original data streams for uncertainty levels below 20%.
- Generated interpretable fuzzy classification rules with reduced antecedent lengths and certainty values in consequent labels.
Conclusions:
- Integrating expert labeling uncertainty into EFNC models is effective for improving performance and interpretability.
- EFNC-U offers a robust solution for handling uncertain data in classification tasks.
- The elicited rules provide valuable insights and knowledge discovery potential for applications like fraud detection.