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Gary Weiss, Kate McCarthy, Bibi Zabar Fordham University

Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?. Gary Weiss, Kate McCarthy, Bibi Zabar Fordham University. Background. Highly skewed data is common Typically more interested in correctly classifying the minority class examples

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Gary Weiss, Kate McCarthy, Bibi Zabar Fordham University

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  1. Cost-Sensitive Learning vs. Sampling:Which is Best for Handling Unbalanced Classes with Unequal Error Costs? Gary Weiss, Kate McCarthy, Bibi Zabar Fordham University

  2. Background • Highly skewed data is common • Typically more interested in correctly classifying the minority class examples • Without special measures, classifier will rarely predict the minority class • A common approach: balance the data • Imposes non-uniform misclassification costs* • If alter training set class distribution from 1:1 to 2:1 then have essentially applied a cost ratio of 2:1 * C. Elkan. The foundations of cost-sensitive learning. IJCAI 2001.

  3. Two Competing Approaches • Cost-sensitive learning algorithm • The algorithm itself handles cost-sensitivity • Does not throw away any data • Sampling • Down-sample the majority class • Discards potentially useful data • Up-sample the minority class • Increases amount of training data • Replicated examples may lead to overfitting

  4. The Question ? • Which method is best? • cost-sensitive learning algorithm • up-sampling • down-sampling • Most prior work compares sampling methods

  5. Experiments • We assume that cost information is known • Since cost info not really provided, we evaluate a variety of cost ratios and reports all results • Classifier performance is evaluated using total cost • Used cost-sensitive C5.0 • Evaluated scenarios whereCFNCFP • All results are based on averages over 10 runs • For cost-sensitive learning, cost info passed in • For sampling approaches • Altered the the training data to “impose” the specified misclassification cost

  6. Fourteen Data Sets

  7. Results: Letter-a Data Set 4% minority 20,000 examples

  8. Weather Data Set 40% minority 5,597 examples

  9. Coding Data Set 50% minority 20,000 examples

  10. Blackjack Data Set 36% minority 15,000 examples

  11. Contraceptive Data Set 23% minority 1,473 examples

  12. Results: 1st/2nd/3rd Place Finishes

  13. Comparison of 3 Methods

  14. Discussion • Results vary widely based on the data set • no method consistently outperforms the other two or even one of the other two • Are there any patterns based on the properties of the data sets?

  15. Discussion II: Patterns • For the four smallest data sets (size < 209) • Up-sampling does by far the best • Down-sampling does poorly since it discards data • For the eight largest data sets (size > 10,000) • Cost-sensitive learning does best • Beats up-sampling on average by 5.5% • Beats down-sampling on average by 5.7% • No clear pattern based on the degree of class imbalance

  16. Discussion III • Why might cost-sensitive learning algorithm perform best for large data sets? • Perhaps this method requires accurate probability estimates in order to perform well • This requires many examples per classification “rule”

  17. Conclusion • No consistent winner between cost-sensitive learning and sampling methods • Substantial differences for specific data sets • Cost-sensitive learning may be best for large data sets • Up-sampling appears best for small data sets

  18. Follow-up Questions • Why isn’t cost-sensitive learning the best? • Can we identify problems with cost-sensitive learners? • Can we improve cost-sensitive learners? • Are we better off not using cost-sensitive learner and using sampling instead?!

  19. Future Work • There are areas for future work • Use additional cost-sensitive learners • Use larger data sets (then cost-sensitive best?) • Include more sophisticated sampling schemes • Don’t assume known costs (ROC analysis) • I believe more comprehensive studies are needed and are underway

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