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Boosting an Associative Classifier

Boosting an Associative Classifier. Presenter ： Chien-Shing Chen Author: Yanmin Sun Yang Wang Andrew K.C. Wong. 2006, TKDE. Outline. Motivation Objective Introduction Weight Strategies for Voting Experiments Conclusions Personal Opinion. Motivation.

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Boosting an Associative Classifier

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  1. Boosting an Associative Classifier Presenter：Chien-Shing Chen Author: Yanmin Sun Yang Wang Andrew K.C. Wong 2006, TKDE

  2. Outline • Motivation • Objective • Introduction • Weight Strategies for Voting • Experiments • Conclusions • Personal Opinion

  3. Motivation • Boosting is a general method for improving the performance of any learning algorithm. • no reported work on boosting associative classifiers

  4. Objective • describe three strategies for voting multiple classifiers in boosting an HPWR classification system • AdaBoost • evidence weight • Hybrid • analyzes the features of these three strategies

  5. Weighting Strategies for Voting • Let εdenotes the weighted training error at each iteration. • weight of evidence provided by x in favor of yi as opposed to other values P(x∩yi) / p(yi)

  6. t=1 x1 x4 t=2 x1 x4

  7. Experiments

  8. Experiments

  9. Experiments

  10. Opinion • Drawback • lack handing with the Class level (predicting attributes) Qualification • Application • any classification problem • Future Work • weight of evidence description • Fourth strategic

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