1. Bagging LING 572 Fei Xia 1/24/06

2. Ensemble methods So far, we have covered several learning methods: FSA, HMM, DT, DL, TBL. Question: how to improve results? One solution: generating and combining multiple predictors –Bagging: bootstrap aggregating –Boosting –…–…

3. Outline An introduction to the bootstrap Bagging: basic concepts (Breiman, 1996) Case study: bagging a treebank parser (Henderson and Brill, ANLP 2000)

4. Introduction to bootstrap

5. Motivation What’s the average price of house prices? From F, get a sample x=(x 1, x 2, …, x n ), and calculate the average u. Question: how reliable is u? What’s the standard error of u? what’s the confidence interval?

6. Solutions One possibility: get several samples from F. Problem: it is impossible (or too expensive) to get multiple samples. Solution: bootstrap

7. The general bootstrap algorithm Let the original sample be L=(x 1,x 2,…,x n ) Repeat B time: –Generate a sample L k of size n from L by sampling with replacement. –Compute for x*.  Now we end up with bootstrap values Use these values for calculating all the quantities of interest (e.g., standard deviation, confidence intervals)

8. An example X=(3.12, 0, 1.57, 19.67, 0.22, 2.20) Mean=4.46 X1=(1.57,0.22,19.67, 0,0,2.2,3.12) Mean=4.13 X2=(0, 2.20, 2.20, 2.20, 19.67, 1.57) Mean=4.64 X3=(0.22, 3.12,1.57, 3.12, 2.20, 0.22) Mean=1.74

9. A quick view of bootstrapping Introduced by Bradley Efron in 1979 Named from the phrase “to pull oneself up by one’s bootstraps”, which is widely believed to come from “the Adventures of Baron Munchausen”. Popularized in 1980s due to the introduction of computers in statistical practice. It has a strong mathematical background. It is well known as a method for estimating standard errors, bias, and constructing confidence intervals for parameters.

10. Bootstrap distribution The bootstrap does not replace or add to the original data. We use bootstrap distribution as a way to estimate the variation in a statistic based on the original data.

11. Sampling distribution vs. bootstrap distribution The population: certain unknown quantities of interest (e.g., mean) Multiple samples  sampling distribution Bootstrapping: –One original sample  B bootstrap samples –B bootstrap samples  bootstrap distribution

12. Bootstrap distributions usually approximate the shape, spread, and bias of the actual sampling distribution. Bootstrap distributions are centered at the value of the statistic from the original sample plus any bias. The sampling distribution is centered at the value of the parameter in the population, plus any bias.

13. Cases where bootstrap does not apply Small data sets: the original sample is not a good approximation of the population Dirty data: outliers add variability in our estimates. Dependence structures (e.g., time series, spatial problems): Bootstrap is based on the assumption of independence. …

14. How many bootstrap samples are needed? Choice of B depends on Computer availability Type of the problem: standard errors, confidence intervals, … Complexity of the problem

15. Resampling methods Boostrap Permutation tests Jackknife: we ignore one observation at each time …

16. Bagging: basic concepts

17. Bagging Introduced by Breiman (1996) “Bagging” stands for “bootstrap aggregating”. It is an ensemble method: a method of combining multiple predictors.

18. Predictors Let L be a training set {(x i, y i ) | x i in X, y i in Y}, drawn from the set Λ of possible training sets. A predictor Φ: X  Y is a function that for any given x, it produces y=Φ(x). A learning algorithm Ψ: Λ  that given any L in Λ, it produces a predictor Φ=Ψ(L) in. Types of predictors: –Classifiers: DTs, DLs, TBLs, … –Estimators: Regression trees –Others: parsers

19. Bagging algorithm Let the original training data be L Repeat B times: –Get a bootstrap sample L k from L. –Train a predictor using L k. Combine B predictors by –Voting (for classification problem) –Averaging (for estimation problem) –…–…

20. Bagging decision trees 1. Splitting the data set into training set T1 and test set T2. 2. Bagging using 50 bootstrap samples. 3. Repeat Steps 1-2 100 times, and calculate average test set misclassification rate.

21. Bagging regression trees Bagging with 25 bootstrap samples. Repeat 100 times.

22. How many bootstrap samples are needed? Bagging decision trees for the waveform task: Unbagged rate is 29.0%. We are getting most of the improvement using only 10 bootstrap samples.

23. Bagging k-nearest neighbor classifiers 100 bootstrap samples. 100 iterations. Bagging does not help.

24. Experiment results Bagging works well for “unstable” learning algorithms. Bagging can slightly degrade the performance of “stable” learning algorithms.

25. Learning algorithms Unstable learning algorithms: small changes in the training set result in large changes in predictions. –Neural network –Decision tree –Regression tree –Subset selection in linear regression Stable learning algorithms: –K-nearest neighbors

26. Case study

27. Experiment settings Henderson and Brill ANLP-2000 paper Parser: Collins’s Model 2 (1997) Training data: sections 01-21 Test data: Section 23 Bagging: Different ways of combining parsing results

28. Techniques for combining parsers (Henderson and Brill, EMNLP-1999) Parse hybridization: combining the substructures of the input parses –Constituent voting –Naïve Bayes Parser switching: selecting one of the input parses –Similarity switching –Naïve Bayes

29. Experiment results Baseline (no bagging): 88.63 Initial (one bag): 88.38 Final (15 bags): 89.17

30. Training corpus size effects

31. Summary Bootstrap is a resampling method. Bagging is directly related to bootstrap. –It uses bootstrap samples to train multiple predictors. –Output of predictors are combined by voting or other methods. Experiment results: –It is effective for unstable learning methods. –It does not help stable learning methods.

32. Uncovered issues How to determine whether a learning method is stable or unstable? Why bagging works for unstable algorithms?