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Home / Proceedings / AAAI Workshop Papers 2000 /

Learning from Imbalanced Data Sets

Contents

  • Machine Learning from Imbalanced Data Sets 101

    Foster Provost

    PDF
  • Open Mind Animals: Insuring the Quality of Data Openly Contributed over the World Wide Web

    David G. Stork, Chuck P. Lam

    PDF
  • Learning from Imbalanced Data Sets: A Comparison of Various Strategies

    Nathalie Japkowicz

    PDF
  • Correlates of State Failure

    Pamela Surko, Alan N. Unger

    PDF
  • Measuring Performance when Positives are Rare

    S. H. Muggleton, C.H. Bryant, and A. Srinivasan

    PDF
  • Feature Scaling in Support Vector Data Descriptions

    David M.J. Tax, Robert P.W. Duin

    PDF
  • Using Autoencoding Networks for Tramp Metal Detection

    V. Bulitko, R. Greiner, R. Kube, and W. Zhou

    PDF
  • A Recognition-Based Alternative to Discrimination-Based Multi-Layer Perceptrons

    Todd Eavis, Nathalie Japkowicz

    PDF
  • Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria

    Chris Drummond, Robert C. Holte

    PDF
  • When Does Imbalanced Data Require more than Cost-Sensitive Learning?

    Dragos Margineantu

    PDF
  • Learning from Imbalanced Data: Rank Metrics and Extra Tasks

    Rich Caruana

    PDF
  • Handling Imbalanced Data Sets in Insurance Risk Modeling

    Edwin P. D. Pednault, Barry K. Rosen, and Chidanand Apte

    PDF
  • Learning to Predict Extremely Rare Events

    Gary M. Weiss, Haym Hirsh

    PDF
  • An Approach to Imbalanced Data Sets Based on Changing Rule Strength

    Jerzy W. Grzymala-Busse, Linda K. Goodwin, Witold J. Grzymala-Busse, and Xinqun Zheng

    PDF

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