论文标题
关于多标签分类器合奏的聚合
On Aggregation in Ensembles of Multilabel Classifiers
论文作者
论文摘要
虽然文献中提出了多种用于多标签分类的合奏方法，但到目前为止，如何汇总整体成员的预测的问题很少受到关注。 In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation.尽管两种方法都将通常用于多标签合奏的投票技术概括，但它们允许明确考虑目标绩效指标。因此，CTP和PTC的混凝土实例可以定制为具体损失函数。在实验上，我们表明，标准投票技术的表现确实超过了CTP和PTC的合适实例，并提供了一些证据表明，CTP在可分解的损失功能方面表现良好，而PTC是不可兼容损失的更好选择。
While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses.
