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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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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 • 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.
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
The Question ? • Which method is best? • cost-sensitive learning algorithm • up-sampling • down-sampling • Most prior work compares sampling methods
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 whereCFNCFP • 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
Results: Letter-a Data Set 4% minority 20,000 examples
Weather Data Set 40% minority 5,597 examples
Coding Data Set 50% minority 20,000 examples
Blackjack Data Set 36% minority 15,000 examples
Contraceptive Data Set 23% minority 1,473 examples
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?
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
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”
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
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?!
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