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Unrealistic Optimism Bias

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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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Related Experiment Videos

Optimism in Active Learning.

Timothé Collet1, Olivier Pietquin2

  • 1CentraleSupélec, MaLIS Research Group, 57070 Metz, France ; GeorgiaTech-CNRS UMI 2958, 57070 Metz, France.

Computational Intelligence and Neuroscience
|December 19, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces three new active learning algorithms for classification tasks. These methods leverage Optimism in the Face of Uncertainty to efficiently reduce training set size and improve classification accuracy.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Active learning aims to minimize training data size for classification.
  • Selecting informative instances is crucial but challenging due to unknown future impact.
  • Balancing exploration and exploitation is key for online error minimization.

Purpose of the Study:

  • To introduce novel active learning algorithms for classification.
  • To apply the Optimism in the Face of Uncertainty strategy to active learning.
  • To evaluate the performance of these new algorithms against existing methods.

Main Methods:

  • Development of three active learning algorithms based on Optimism in the Face of Uncertainty.
  • Estimation of instance-label pair impact using existing training data.
  • Online minimization of classification error through exploration-exploitation tradeoff.

Main Results:

  • Experimental validation on benchmark and real-world datasets.
  • Demonstrated positive comparison against state-of-the-art active learning techniques.
  • Effective reduction in classification error through optimized training set construction.

Conclusions:

  • The proposed active learning algorithms are effective and competitive.
  • Optimism in the Face of Uncertainty provides a robust framework for active learning.
  • These algorithms offer a promising approach for efficient classification model training.