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Boosting: Combining Weak Classifiers for Powerful Predictions

Boosting trains classifiers in a sequence, focusing on incorrect cases each round and combining them for final predictions. Adaptively weighing data cases, boosting rounds learn new classifiers to create a strong final classifier with high training error rate. Monitor with hold-out set.

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Boosting: Combining Weak Classifiers for Powerful Predictions

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  1. Boosting • Main idea: • train classifiers (e.g. decision trees) in a sequence. • a new classifier should focus on those cases which were incorrectly classified in the last round. • combine the classifiers by letting them vote on the final prediction (like bagging). • each classifier could be (should be) very “weak”, e.g. a decision stump.

  2. Boosting Intuition • We adaptively weigh each data case. • Data cases which are wrongly classified get high weight (the algorithm will focus on them) • Each boosting round learns a new (simple) classifier on the weighed dataset. • These classifiers are weighed to combine them into a single powerful classifier. • Classifiers that obtain low training error rate have high weight. • We stop by using monitoring a hold out set (cross-validation).

  3. Example

  4. Boosting in a Picture boosting rounds training cases correctly classified training case has large weight in this round this DT has a strong vote.

  5. And in animation Original Training set : Equal Weights to all training samples Taken from “A Tutorial on Boosting” by Yoav Freund and Rob Schapire

  6. AdaBoost(Example) ROUND 1

  7. AdaBoost(Example) ROUND 2

  8. AdaBoost(Example) ROUND 3

  9. AdaBoost(Example)

  10. AdaBoost • Given:                                                      where • Initialise •                           . • For                               : • Find the classifier                                                         that minimizes the error with respect to the distribution Dt: • Prerequisite: εt < 0.5, otherwise stop. • Choose                     , typically                                            where εt is the weighted error rate of classifier ht. • Update: • where Zt is a normalisation factor (chosen so that Dt + 1 will be a distribution). • Output the final classifier: • The equation to update the distribution Dt is constructed so that: • Thus, after selecting an optimal classifier       for the distribution        , the examples     that the classifier • identified correctly are weighted less and those that it identified incorrectly are weighted more. • Therefore, when the algorithm is testing the classifiers on the distribution            , it will select a • classifier that better identifies those examples that the previous classifer missed.

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