iBoost: Boosting Using an instance-Based Exponential Weighting Scheme

摘要

Recently, Freund, Mansour and Schapire established that using exponential weighting scheme in combining classifiers reduces the problem of overfitting. Also, Helmbold, Kwek and Pitt that showed in the prediction using a pool of experts framework an instance based weighting scheme improves performance. Motivated by these results, we propose here an instance-based exponential weighting scheme in which the weights of the base classifiers are adjusted according to the test instance x. Here, a competency classifier c_i is constructed for each base classifier h_i to predict whether the base classifier's guess of x's label can be trusted and adjust the weight of h_i accordingly. We show that this instance-based exponential weighting scheme enhances the performance of AdaBoost.