1. Ensemble Methods
An ensemble method constructs a set of base classifiers from the training data
Ensemble or Classifier Combination
Predict class label of previously unseen records by aggregating predictions made by multiple classifiers

2. General Idea

3. Why does it work? Suppose there are 25 base classifiers
Each classifier has error rate,  = 0.35
Assume classifiers are independent
Probability that the ensemble classifier makes a wrong prediction:

4. Methods
By manipulating the training dataset: a classifier is built for a sampled subset of the training dataset.
Two ensemble methods: bagging (bootstrap averaging) and boosting.

5. Characteristics
Ensemble methods work better with unstable classifiers.
Base classifiers that are sensitive to minor perturbations in the training set.
For example, decision trees and ANNs.
The variability among training examples is one of the primary sources of errors in a classifier.

6. Bias-Variance Decomposition
Consider the trajectories of a projectile launched at a particular angle. The observed distance can be divided into 3 components.
Force (f) and angle (θ)
Suppose the target is t, but the projectile hits at x at distance d away from t.

7. Two Decision Trees (1)

8. Two Decision Trees (2)
Bias: The stronger the assumptions made by a classifier about the nature of its decision boundary, the larger the classifier’s bias will be.
A smaller tree has a stronger assumption.
An algorithm cannot learn the target.
Variance: Variability in the training data affects the expected error, because different compositions of the training set may lead to different decision boundaries.
Intrinsic noise in the target class.
Target class for some domain can be non- deterministic.
Same attributes values with different class labels.

9. Bagging Sampling with replacement
Build classifier on each bootstrap sample
Each sample has probability 1 - (1 – 1/n)n of being selected. When n is large, a bootstrap sample Di contains about 63.2% of the training data.

10. Bagging Algorithm

11. A Bagging Example (1)
Consider a one-level binary decision tree x <= k where k is a split point to minimize the entropy.
Without bagging, the best decision stump is
x <= 0.35 or x >= 0.75

12. A Bagging Example (2)

13. A Bagging Example (3)

14. A Bagging Example (4)

15. Summary on Bagging
Bagging improves generalization error by reducing the variance of the base classifier.
Bagging depends on the stability of the base classifier.
If a base classifier is unstable, bagging helps to reduce the errors associated with random fluctuations in the training data.
If a base classifier is stable, then the error of the ensemble is primarily caused by bias in the base classifier. Bagging may make error larger, because the sample size is 37% smaller.

16. Boosting
An iterative procedure to adaptively change distribution of training data by focusing more on previously misclassified records
Initially, all N records are assigned equal weights
Unlike bagging, weights may change at the end of boosting round
The weights can be used by a base classifier to learn a model that is biased toward higher- weight examples.

17. Boosting
Records that are wrongly classified will have their weights increased
Records that are classified correctly will have their weights decreased
Example 4 is hard to classify
Its weight is increased, therefore it is more likely to be chosen again in subsequent rounds

18. Example: AdaBoost Base classifiers: C1, C2, …, CT Error rate:
Importance of a classifier:

19. Example: AdaBoost Weight update:
If any intermediate rounds produce error rate higher than 50%, the weights are reverted back to 1/n and the resampling procedure is repeated
Classification:

21. A Boosting Example (1)
Consider a one-level binary decision tree x <= k where k is a split point to minimize the entropy.
Without bagging, the best decision stump is
x <= 0.35 or x >= 0.75

22. A Boosting Example (2)

23. A Boosting Example (3)