多标签分类:一种使用决策树学习标签关系和防止循环依赖的新方法:关系识别和去除循环(3RC)

Hamza Lotf, M. Ramdani
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引用次数: 2

摘要

多标签分类(MLC)是机器学习的一个领域,它包括通过为每个实例分配一组标签而不是一个标签来分类数据。这些标签或类之间可以有依赖关系。忽略这些信息会影响分类的预测质量。在构建分类器时,考虑这些依赖关系或忽略它们,每种依赖关系都有其缺点。第一种方法促进了学习错误的传播,增加了任务的复杂性,特别是在类之间存在周期性关系的情况下。而第二种方法可能给出不一致的预测。有多种方法用于解决多标签分类任务,其中一些考虑了标签的依赖性,而另一些则认为它们是独立的。一种称为PSI-MC的新方法提出了一种新的方法来学习标签之间的关系,而无需固定预定义的结构。我们提出了一种使用与PSI- MC相同原理的方法,并改进了消除循环的方法。最后,我们给出了在四个不同的数据集上测试我们的新方法的结果。根据四个指标,我们提出的(3RC)方法比二元相关、RAKEL和MLKNN方法要好得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Label Classification: A Novel approach using decision trees for learning Label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC)
Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.
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