Double-Base Asymmetric AdaBoost
This is an incremental improvement for machine learning practitioners dealing with large-scale boosting tasks, as it dramatically speeds up training without sacrificing performance.
The paper tackles the problem of training time inefficiency in cost-sensitive boosting by proposing AdaBoostDB, a new asymmetric boosting scheme that saves over 99% training time compared to Cost-Sensitive AdaBoost while providing the same results.
Based on the use of different exponential bases to define class-dependent error bounds, a new and highly efficient asymmetric boosting scheme, coined as AdaBoostDB (Double-Base), is proposed. Supported by a fully theoretical derivation procedure, unlike most of the other approaches in the literature, our algorithm preserves all the formal guarantees and properties of original (cost-insensitive) AdaBoost, similarly to the state-of-the-art Cost-Sensitive AdaBoost algorithm. However, the key advantage of AdaBoostDB is that our novel derivation scheme enables an extremely efficient conditional search procedure, dramatically improving and simplifying the training phase of the algorithm. Experiments, both over synthetic and real datasets, reveal that AdaBoostDB is able to save over 99% training time with regard to Cost-Sensitive AdaBoost, providing the same cost-sensitive results. This computational advantage of AdaBoostDB can make a difference in problems managing huge pools of weak classifiers in which boosting techniques are commonly used.