双基地不对称AdaBoost
Double-Base Asymmetric AdaBoost
摘要：
基于使用不同的指数基来定义类相关的误差界，提出了一种新的高效的非对称Boosting方案，称为AdaBoostDB（Double-Base）。支持一个完全的理论推导过程中，与文献中的大多数其他方法不同，我们的算法保留了所有的正式保证和原始（成本不敏感）AdaBoost的属性，类似于国家的最先进的成本敏感AdaBoost算法。然而，AdaBoostDB的关键优势在于，我们的新推导方案实现了非常高效的条件搜索过程，大大改进和简化了算法的训练阶段。在合成和真实数据集上的实验表明，AdaBoostDB能够节省99%以上的成本敏感AdaBoost训练时间，提供相同的成本敏感结果。AdaBoostDB的这种计算优势可以在管理大量弱分类器的问题中发挥作用，其中通常使用boosting技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
