Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
| Hi-index | 0.03 |
It is well known that the boosting-like algorithms, such as AdaBoost and many of its modifications, may over-fit the training data when the number of boosting iterations becomes large. Therefore, how to stop a boosting algorithm at an appropriate iteration time is a longstanding problem for the past decade (see Meir and Ratsch, 2003). Buhlmann and Yu (2005) applied model selection criteria to estimate the stopping iteration for L"2Boosting, but it is still necessary to compute all boosting iterations under consideration for the training data. Thus, the main purpose of this paper is focused on studying the early stopping rule for L"2Boosting during the training stage to seek a very substantial computational saving. The proposed method is based on a change point detection method on the values of model selection criteria during the training stage. This method is also extended to two-class classification problems which are very common in medical and bioinformatics applications. A simulation study and a real data example to these approaches are provided for illustrations, and comparisons are made with LogitBoost.