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Adaboost (Freund and Schapire, 1997)

  • Given a set of d class-labeled tuples, (X1, y1), …, (Xd, yd)
  • Initially, all the weights of tuples are set the same (1/d)
  • Generate k classifiers in k rounds. At round i,
    • Tuples from D are sampled (with replacement) to form a training set Di of the same size
    • Each tuple’s chance of being selected is based on its weight
    • A classification model Mi is derived from Di
    • Its error rate is calculated using Di as a test set
    • If a tuple is misclassified, its weight is increased, o.w. it is decreased
  • Error rate: err(Xj) is the misclassification error of tuple Xj. Classifier Mi error rate is the sum of the weights of the misclassified tuples: 

\[error(M_{i})=\sum_{j}^{d}w_{j}\times err (X_{j})\]

  • The weight of classifier Mi’s vote is

\[log \frac{1-error(M_{i})}{error(M_{i})}\]


Speaker notes:

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