Learning from Imbalanced Data Sets
Papers from the AAAI Workshop
Nathalie Japkowicz, Program Chair
Technical Report WS-00-05 published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Machine Learning from Imbalanced Data Sets 101 / 1
Foster Provost
Open Mind Animals: Insuring the Quality of Data Openly Contributed over the World Wide Web / 4
David G. Stork and Chuck P. Lam
Learning from Imbalanced Data Sets: A Comparison of Various Strategies / 10
Nathalie Japkowicz
Correlates of State Failure / 16
Pamela Surko and Alan N. Unger
Measuring Performance when Positives are Rare / 18
S. H. Muggleton, C.H. Bryant, and A. Srinivasan
Feature Scaling in Support Vector Data Descriptions / 25
David M. J. Tax and Robert P.W. Duin
Using Autoencoding Networks for Tramp Metal Detection / 31
V. Bulitko, R. Greiner, R. Kube, and W. Zhou
A Recognition-Based Alternative to Discrimination-Based Multi-Layer Perceptrons / 32
Todd Eavis and Nathalie Japkowicz
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria / 39
Chris Drummond and Robert C. Holte
When Does Imbalanced Data Require more than Cost-Sensitive Learning? / 47
Dragos Margineantu
Learning from Imbalanced Data: Rank Metrics and Extra Tasks / 51
Rich Caruana
Handling Imbalanced Data Sets in Insurance Risk Modeling / 58
Edwin P. D. Pednault, Barry K. Rosen, and Chidanand Apte
Learning to Predict Extremely Rare Events / 64
Gary M. Weiss and Haym Hirsh
An Approach to Imbalanced Data Sets Based on Changing Rule Strength / 69
Jerzy W. Grzymala-Busse, Linda K. Goodwin, Witold J. Grzymala-Busse, and Xinqun Zheng
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