Adaptive Boosting

Adaptive Boosting#

Adaptive boosting or AdaBoost is an ensemble method that:

  • trains multiple leanrers on weighted sample points

  • use different weight for each learner

  • incraese weight of misclassified sample points

  • give bigger votes to more accurate learners

Let’s take the binary classification example \(y\) with design matrix \(X\).

  • Train \(T\) classifiers \(G_1, \ldots, G_T\)

  • The weight for sample point \(X_i\) in \(G_t\) grows according to how \(G_1, \ldots G_{t-1}\) misclassified it and shrinks on correct classification.

  • Train \(G_t\) to try harder to correctly classify points with larger weights.

  • The metalearner is a linear combination of the ensemble.

    \[ M(z) = \sum_{t=1}^T \beta_t G_t(z) \]

Algorithm#

On each iteration \(t\):

  1. Initialize weights \(w_i = \frac{1}{n}\)

  2. Find \(G_t\) that minimizes the metalearner: $\( L(\rho, \ell) = e^{-\rho \ell} \qquad \ell \in \{-1, 1\} \)$

    \[\begin{split} \begin{align*} \mathcal L &= \frac{1}{n}\sum_{n=1}^n L(M(X_i), y_i), \qquad M(X_i)=\sum_{t=1}^T \beta_t G_t (X_i)\\ &= \sum{w_i^{(T)}}e^{-\beta_Ty_iG_T(X_i)} \end{align*} \end{split}\]
    \[ w_i^{(T)} = \prod_{t=1}^{T-1}\exp\left[-\beta_ty_iG_t(X_i)\right] \]
    \[ \mathcal L = e^{-\beta_T}\sum_{i=1}^n w_i^{(T)} + (e^{\beta_T} - e^{-\beta_T})\underbrace{\sum_{y_i \neq G_T(X_i)}w_i^{T}}_\text{sum of misclassified weights} \]
    \[ w_i^{(T+1)} = w_i^{(T)}e^{-\beta_Ty_iG_T(X_i)} \]

  3. Find \(\beta_t\) that minimizes the metalearner:

    \[ \frac{\partial}{\partial \beta_T}\mathcal L = -e^{-\beta_T}\sum_{i=1}^{n}w_i^{(T)}+\left(e^{\beta_T}+e^{-\beta_T} \right)\sum_{y_i \neq G_T(X_i)}w_i^{(T)} \]
    \[ 0 = -1 + (e^{2\beta_T} + 1)\text{err}_T, \qquad \text{err}_T = \frac{\text{weight wrong}}{\text{all weight}} \]
    \[ \beta_T = \frac{1}{2}\log\left(\frac{1-\text{err}_T}{\text{err}_T}\right) \]

  4. Reweight points \(w_i = w_i e^{\beta_t y_i G_t(i)}\)

Facts and Heuristics#

  • Best for short tree (depth ~3-5) ensembles.

  • Posterior probability can be approximated via:

    \[ P(Y \mid x) \approx \frac{1}{1 + e^{-2M(x)}} \]

  • If every learner has \(s \ge \mu \ge 50\) accuracy score, the metalearner training accuracy will be 100%.

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