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Related Concept Videos

Retrieval01:12

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...

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A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval.

S Joshua Swamidass1, Chloé-Agathe Azencott, Kenny Daily

  • 1Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA.

Bioinformatics (Oxford, England)
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

We introduce the Concentrated ROC (CROC) framework to improve classifier performance evaluation for early retrieval tasks. This new method offers better visualization and metrics for top-ranked predictions, crucial in fields like drug discovery.

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Area of Science:

  • Machine Learning
  • Bioinformatics
  • Information Retrieval

Background:

  • Traditional Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) metrics are insufficient for evaluating classifier performance when only the top predictions are of interest.
  • The 'early retrieval' problem, common in information retrieval and drug discovery, requires specialized evaluation methods.

Purpose of the Study:

  • To develop a novel framework for evaluating classifier performance focused on the top-ranked predictions (early retrieval).
  • To introduce new metrics and visualizations that effectively address the limitations of traditional ROC/AUC in early retrieval scenarios.

Main Methods:

  • Development of the general Concentrated ROC (CROC) framework, which magnifies relevant portions of the ROC curve using continuous coordinate transformations.
  • Derivation and analysis of magnification functions and resulting CROC curves.
  • Proposal of CROC(exp), an exponential transform of the ROC curve, as a novel metric and visualization tool.

Main Results:

  • The CROC framework provides a magnified view of the ROC curve, enhancing the assessment of early retrieval performance.
  • The area under the CROC curve (AUC[CROC]) serves as a quantitative measure for early retrieval.
  • Demonstration on a drug discovery problem showed CROC(exp) effectively discriminates the performance of different predictors with high statistical power.

Conclusions:

  • The CROC framework and its associated metrics, particularly CROC(exp), offer a principled, flexible, and effective solution for measuring and visualizing early retrieval performance.
  • This approach overcomes the limitations of traditional ROC/AUC for tasks prioritizing top-ranked predictions.
  • Publicly available code and datasets facilitate the application and further development of CROC methods.