A top-down supervised learning approach to hierarchical multi-label classification in networks

被引：0
作者：
Miguel Romero
Jorge Finke
Camilo Rocha
机构：
[1] Pontificia Universidad Javeriana,Department of Electronics and Computer Science
来源：
Applied Network Science | / 7卷
关键词：
Hierarchical classification; Supervised learning; XGBoost; Top-down approach; Gene function prediction;
D O I：
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学科分类号：
摘要：
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.
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