Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers

1. New York University Stern School Get Another Label?Improving Data Quality and Data Mining Using Multiple, Noisy Labelers Victor Sheng Foster Provost Panos Ipeirotis

2. Outsourcing KDD preprocessing Traditionally, data mining teams have invested substantial internal resources in data formulation, information extraction, cleaning, and other preprocessing Raghu from his Innovation Lecture “the best you can expect are noisy labels” Now, we can outsource preprocessing tasks, such as labeling, feature extraction, verifying information extraction, etc. using Mechanical Turk, Rent-a-Coder, etc. quality may be lower than expert labeling (much?) but low costs can allow massive scale The ideas may apply also to focusing user-generated tagging, crowdsourcing, etc. 2

3. ESP Game (by Luis von Ahn) 3

4. Other “free” labeling schemes • Open Mind initiative (www.openmind.org) • Other gwap games • Tag a Tune • Verbosity (tag words) • Matchin (image ranking) • Web 2.0 systems? • Can/should tagging be directed?

5. Noisy labels can be problematic Many tasks rely on high-quality labels for objects: learning predictive models searching for relevant information finding duplicate database records image recognition/labeling song categorization Noisy labels can lead to degraded task performance 5

6. Here, labels are values for target variable Quality and Classification Performance Labeling quality increases  classification quality increases P = 1.0 P = 0.8 P = 0.6 P = 0.5

7. Summary of results • Repeated labeling can improve data quality and model quality(but not always) • When labels are noisy, repeated labeling can be preferable to single labeling even when labels aren’t particularly cheap • When labels are relatively cheap, repeated labeling can do much better (omitted) • Round-robin repeated labeling does well • Selective repeated labeling improves substantially

8. Majority Voting and Label Quality • Ask multiple labelers, keep majority label as “true” label • Quality is probability of being correct P=1.0 P=0.9 P=0.8 P is probabilityof individual labelerbeing correct P=0.7 P=0.6 P=0.5 P=0.4

9. Tradeoffs for Modeling • Get more labels  Improve label quality  Improve classification • Get more examples  Improve classification P = 1.0 P = 0.8 P = 0.6 P = 0.5

10. Basic Labeling Strategies • Single Labeling • Get as many data points as possible • one label each • Round-robin Repeated Labeling • Fixed Round Robin (FRR) • keep labeling the same set of points • Generalized Round Robin (GRR) • repeatedly-label data points, giving next label to point with fewest so far

11. Fixed Round Robin vs. Single Labeling FRR (100 examples) SL p= 0.6, labeling quality #examples =100 With high noise, repeated labeling better than single labeling

12. Fixed Round Robin vs. Single Labeling Single FRR (50 examples) p= 0.8, labeling quality #examples =50 With low noise, more (single labeled) examples better

13. Gen. Round Robin vs. Single Labeling P: labeling quality k: #labels P=0.6, k=5 GRR SL Repeated labeling is better than single labeling

14. Tradeoffs for Modeling Get more labels  Improve label quality  Improve classification Get more examples  Improve classification P = 1.0 P = 0.8 P = 0.6 P = 0.5 15

15. Selective Repeated-Labeling • We have seen: • With enough examples and noisy labels, getting multiple labels is better than single-labeling • When we consider costly preprocessing, the benefit is magnified (omitted -- see paper) • Can we do better than the basic strategies? • Key observation: we have additional information to guide selection of data for repeated labeling • the current multiset of labels • Example: {+,-,+,+,-,+} vs. {+,+,+,+}

16. Natural Candidate: Entropy • Entropy is a natural measure of label uncertainty: • E({+,+,+,+,+,+})=0 • E({+,-, +,-, +,- })=1 Strategy: Get more labels for examples with high-entropy label multisets

17. What Not to Do: Use Entropy Improves at first, hurts in long run

18. Why not Entropy • In the presence of noise, entropy will be high even with many labels • Entropy is scale invariant (3+ , 2-) has same entropy as (600+ , 400-)

19. Estimating Label Uncertainty (LU) • Observe +’s and –’s and compute Pr{+|obs} and Pr{-|obs} • Label uncertainty = tail of beta distribution Beta probability density function SLU 0.5 0.0 1.0

20. Label Uncertainty • p=0.7 • 5 labels(3+, 2-) • Entropy ~ 0.97 • CDFb=0.34

21. Label Uncertainty • p=0.7 • 10 labels(7+, 3-) • Entropy ~ 0.88 • CDFb=0.11

22. Label Uncertainty • p=0.7 • 20 labels(14+, 6-) • Entropy ~ 0.88 • CDFb=0.04

23. Label Uncertainty vs. Round Robin similar results across a dozen data sets

24. Recall:Gen. Round Robin vs. Single Labeling P: labeling quality k: #labels P=0.6, k=5 GRR SL Multi-labeling is better than single labeling

25. Label Uncertainty vs. Round Robin 26 similar results across a dozen data sets

26. ? - - - - + + - - - - + + + + - - - - - - - - + + + + + + - - - - + + - - - - + + - - - - + + - - - - + + ? Another strategy:Model Uncertainty (MU) • Learning a model of the data provides an alternative source of information about label certainty • Model uncertainty: get more labels for instances that cannot be modeled well • Intuition? • for data quality, low-certainty “regions” may be due to incorrect labeling of corresponding instances • for modeling: why improve training data quality if model already is certain there?

27. Yet another strategy:Label & Model Uncertainty (LMU) • Label and model uncertainty (LMU): avoid examples where either strategy is certain

28. Comparison Model Uncertainty alone also improves quality Label & Model Uncertainty Label Uncertainty GRR

29. Across 12 domains, LMU is always better than GRR. LMU is statistically significantly better than LU and MU. Comparison: Model Quality Label & Model Uncertainty

30. Summary of results • Micro-task outsourcing (e.g., MTurk, RentaCoder ESP game) has changed the landscape for data formulation • Repeated labeling can improve data quality and model quality(but not always) • When labels are noisy, repeated labeling can be preferable to single labeling even when labels aren’t particularly cheap • When labels are relatively cheap, repeated labeling can do much better (omitted) • Round-robin repeated labeling can do well • Selective repeated labeling improves substantially

31. Opens up many new directions… • Strategies using “learning-curve gradient” • Estimating the quality of each labeler • Example-conditional quality • Increased compensation vs. labeler quality • Multiple “real” labels • Truly “soft” labels • Selective repeated tagging

32. Thanks!Q & A?