论文标题
构建合奏堆栈的提升方法
A Boosting Approach to Constructing an Ensemble Stack
论文作者
论文摘要
提出了一种用于分类的进化合奏学习方法，其中使用促进来构建一堆程序。提升的每种应用都识别单个冠军和剩余数据集，即迄今为止尚未正确分类的培训记录。下一个程序仅针对残差进行训练，该过程迭代为止，直到一定的最大合奏尺寸或没有其他残留剩余为止。针对剩余数据集的培训会积极降低培训成本。将合奏作为堆栈部署还意味着只有一个分类器才能进行预测，从而提高可解释性。进行基准测试研究是为了说明竞争力，以当前最先进的进化集合学习算法的预测准确性，同时提供简单级数的解决方案。使用高基数数据集进行进一步的基准测试表明，所提出的方法也比XGBoost更准确，更有效。
An approach to evolutionary ensemble learning for classification is proposed in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.
