论文标题
极端梯度增强的多标签树，用于动态分类器链
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
论文作者
论文摘要
分类器链是多标签分类中的关键技术，因为它允许有效地考虑标签依赖项。但是，根据标签的静态顺序对分类器进行对齐。在动态分类器链（DCC）的概念中，为每个预测选择标签排序，取决于手头的相应实例。我们将这一概念与极端梯度增强的树（XGBoost）（一种有效且可扩展的最新技术）相结合，并将DCC纳入XGBoost的快速多标签扩展中，我们可以公开使用。由于只能预测积极的标签，而且通常只有很少的标签，因此培训成本可以大大降低。此外，正如11个数据集中的实验所示，链的长度可以更好地控制先前预测的使用，因此可以更好地控制一个人想要优化的度量。
Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique, and incorporate DCC in a fast multi-label extension of XGBoost which we make publicly available. As only positive labels have to be predicted and these are usually only few, the training costs can be further substantially reduced. Moreover, as experiments on eleven datasets show, the length of the chain allows for a more control over the usage of previous predictions and hence over the measure one want to optimize.
