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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Ensemble Methods l An ensemble method constructs a set of base classifiers from the training data –Ensemble or Classifier Combination l Predict class label of previously unseen records by aggregating predictions made by multiple classifiers

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 General Idea

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3 Why does it work? l 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:

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4 Methods l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5 Characteristics l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 6 Bias-Variance Decomposition l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7 Two Decision Trees (1)

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 8 Two Decision Trees (2) l 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. l Variance: Variability in the training data affects the expected error, because different compositions of the training set may lead to different decision boundaries. l Intrinsic noise in the target class. –Target class for some domain can be non- deterministic. –Same attributes values with different class labels.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 9 Bagging l Sampling with replacement l Build classifier on each bootstrap sample l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 10 Bagging Algorithm

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 11 A Bagging Example (1) l Consider a one-level binary decision tree x <= k where k is a split point to minimize the entropy. l Without bagging, the best decision stump is –x = 0.75

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 12 A Bagging Example (2)

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 13 A Bagging Example (3)

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 14 A Bagging Example (4)

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 15 Summary on Bagging l Bagging improves generalization error by reducing the variance of the base classifier. l Bagging depends on the stability of the base classifier. l If a base classifier is unstable, bagging helps to reduce the errors associated with random fluctuations in the training data. l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 16 Boosting l 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.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 17 Boosting l Records that are wrongly classified will have their weights increased l 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

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 18 Example: AdaBoost l Base classifiers: C 1, C 2, …, C T l Error rate: l Importance of a classifier:

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19 Example: AdaBoost l Weight update: l If any intermediate rounds produce error rate higher than 50%, the weights are reverted back to 1/n and the resampling procedure is repeated l Classification:

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 20

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21 A Boosting Example (1) l Consider a one-level binary decision tree x <= k where k is a split point to minimize the entropy. l Without bagging, the best decision stump is –x = 0.75

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 22 A Boosting Example (2)

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 23 A Boosting Example (3)

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