Cost-sensitive boosting algorithms: Do we really need them?

We provide a unifying perspective for two decades of work on cost-sensitive Boosting algorithms. When analyzing the literature 1997-2016, we find 15 distinct cost-sensitive variants of the original algorithm; each of these has its own motivation and claims to superiority-so who should we believe? In this work we critique the Boosting literature using four theoretical frameworks: Bayesian decision theory, the functional gradient descent view, margin theory, and probabilistic modelling. Our finding is that only three algorithms are fully supported-and the probabilistic model view suggests that all require their outputs to be calibrated for best performance. Experiments on 18 datasets across 21 degrees of imbalance support the hypothesis-showing that once calibrated, they perform equivalently, and outperform all others. Our final recommendation-based on simplicity, flexibility and performance-is to use the original Adaboost algorithm with a shifted decision threshold and calibrated probability estimates.