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Tree-Shaped Ensemble of Multi-Label Classifiers using Grammar-Guided Genetic Programming

机译：使用语法指导的遗传规划的多标签分类器的树形集成

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Multi-label classification paradigm has had a growing interest because of the emergence of a large number of classification problems where each of the instances of the data can be associated with several output labels simultaneously. Several ensemble methods were proposed to solve the multilabel classification problem. However, most of them simply create diversity in the ensemble by following a random procedure and give the same importance to all members. In this paper, we propose a Grammar-Guided Genetic Programming algorithm to build ensembles of multi-label classifiers. Given a pool of multilabel classifiers, each of them modeling dependencies among a subset of k labels, they are combined into a tree-shaped ensemble. At each node of the tree, predictions of its children nodes are combined, while each leaf represents a classifier from the pool. We propose two configurations for the method: using a fixed value of k for all classifiers in the pool, or using a variable value of k for each classifier, thus being able to capture relationships among groups of labels of different size in the ensemble. The experiments performed over sixteen multi-label dataset and using five evaluation metrics demonstrated that our method performs significantly better than the state-of-the-art ensembles of multilabel classifiers.
机译：由于大量分类问题的出现，多标签分类范例引起了越来越多的关注，其中数据的每个实例可以同时与多个输出标签关联。提出了几种集成方法来解决多标签分类问题。但是，它们中的大多数仅通过遵循随机过程即可在整体中创建多样性，并且对所有成员都具有相同的重要性。在本文中，我们提出了一种语法指导的遗传规划算法，以构建多标签分类器的集合。给定一个多标签分类器池，它们每个都对k个标签子集之间的依存关系进行建模，将它们组合成一个树形集合。在树的每个节点上，将合并其子节点的预测，而每个叶子代表池中的分类器。我们为该方法提出两种配置：对池中的所有分类器使用k的固定值，或对每个分类器使用k的可变值，从而能够捕获集合中大小不同的标签组之间的关系。在16个多标签数据集上进行的实验和使用5个评估指标表明，我们的方法的性能明显优于最新的多标签分类器。

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