Hierarchical multi-label classification with chained neural networks

Jonatas Wehrmann, Rodrigo C. Barros, S. N. D. Dôres, R. Cerri
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引用次数: 37

Abstract

In classification tasks, an object usually belongs to one class within a set of disjoint classes. In more complex tasks, an object can belong to more than one class, in what is conventionally termed multi-label classification. Moreover, there are cases in which the set of classes are organised in a hierarchical fashion, and an object must be associated to a single path in this hierarchy, defining the so-called hierarchical classification. Finally, in even more complex scenarios, the classes are organised in a hierarchical structure and the object can be associated to multiple paths of this hierarchy, defining the problem investigated in this article: hierarchical multi-label classification (HMC). We address a typical problem of HMC, which is protein function prediction, and for that we propose an approach that chains multiple neural networks, performing both local and global optimisation in order to provide the final prediction: one or multiple paths in the hierarchy of classes. We experiment with four variations of this chaining process, and we compare these strategies with the state-of-the-art HMC algorithms for protein function prediction, showing that our novel approach significantly outperforms these methods.
链式神经网络的分层多标签分类
在分类任务中,一个对象通常属于一组互不关联的类中的一个类。在更复杂的任务中,一个对象可以属于多个类,这通常被称为多标签分类。此外,在某些情况下,类集以分层方式组织,并且必须将对象关联到该层次结构中的单个路径,从而定义了所谓的分层分类。最后,在更复杂的场景中,类以层次结构组织,对象可以与该层次结构的多条路径相关联,这就定义了本文研究的问题:层次多标签分类(HMC)。我们解决了HMC的一个典型问题,即蛋白质功能预测,为此我们提出了一种连接多个神经网络的方法,执行局部和全局优化,以提供最终预测:类层次结构中的一条或多条路径。我们实验了这种链化过程的四种变体,并将这些策略与最先进的蛋白质功能预测HMC算法进行了比较,结果表明我们的新方法明显优于这些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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