Sampling Approaches to Learning from Imbalanced Datasets

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Sampling Approaches to Learning from Imbalanced
Datasets

• Naoki Abe
• IBM T.J. Watson Research Center
• Based on joint work with
• Bianca Zadrozny
• University of California, San Diego
• Hiroshi Mamitsuka
• Kyoto University
• John Langfod, Edwin Pednault, Chid Apte et al
• IBM T.J. Watson Research Center

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Outline

• Introduction
• Industrial applications and learning from
  imbalanced datasets
• Review Past approaches to learning from
  imbalanced datasets
• Sampling Approaches to Learning from Imbalanced
  Dataset
• Selective sampling based on query learning
• Sampling for cost-sensitive learning
• Discussion

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Industrial Applications and the Issue of
Imbalanced Dataset

• Industrial Applications
• Hardware Fault Detection (e.g. Apte, Weiss, Grout
  93)
• Faults are rare but very costly
• Insurance Risk Modeling (e.g. Pednault, Rosen,
  Apte