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Related Concept Videos

Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

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Related Experiment Video

Updated: Jun 2, 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

Generalized and Heuristic-Free Feature Construction for Improved Accuracy.

Wei Fan1, Erheng Zhong, Jing Peng

  • 1IBM T. J. Watson Research Center, USA. weifan@us.ibm.com , ov1@us.ibm.com , yanr@us.ibm.com.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining
|May 6, 2011
PubMed
Summary

This study introduces a novel framework for automated feature construction, enhancing machine learning model accuracy by up to 9% and AUC by 28% through localized feature evaluation and domain-knowledge-free search strategies.

Related Experiment Videos

Last Updated: Jun 2, 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
  • Data Science
  • Artificial Intelligence

Background:

  • Machine learning algorithms require data in feature vector format.
  • Separating classes in the original feature space can be challenging.
  • Feature transformation may reveal hidden discriminative information.

Purpose of the Study:

  • To develop an automated and general framework for feature construction.
  • To overcome limitations of exhaustive feature enumeration and global evaluation.
  • To improve classification accuracy without domain expertise.

Main Methods:

  • A divide-and-conquer approach to avoid exhaustive enumeration.
  • Local feature construction and evaluation in subspaces with high error.
  • Domain knowledge-free weighting rules-based search with performance guarantees.

Main Results:

  • Significant improvements in accuracy (up to 9%) and AUC (up to 28%).
  • Effective performance across balanced, skewed, and high-dimensional datasets.
  • Demonstrated improvement over various inductive learners.

Conclusions:

  • The proposed framework effectively automates feature construction.
  • Localized evaluation and domain-independent search enhance discriminative power.
  • The method offers a generalizable approach to improve classification performance.